human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "interleaved text image deep mining on a large scale radiology database for automated image interpretation"
  Abstract: Despite tremendous progress in computer vision, there has not been an attempt for machine learning on very large-scale medical image databases. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System. With natural language processing, we mine a collection of representative ~216K two-dimensional key images selected by clinicians for diagnostic reference, and match the images with their descriptions in an automated manner. Our system interleaves between unsupervised learning and supervised learning on document- and sentence-level text collections, to generate semantic labels and to predict them given an image. Given an image of a patient scan, semantic topics in radiology levels are predicted, and associated key-words are generated. Also, a number of frequent disease types are detected as present or absent, to provide more specific interpretation of a patient scan. This shows the potential of large-scale learning and prediction in electronic patient records available in most modern clinical institutions.

Connected through hyperedge HE-1 (induced by source paper "transfusion understanding transfer learning for medical imaging"):
  with members:
    - "similarity of neural network representations revisited"
    - "investigating multilingual nmt representations at scale"
    - "the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives"
    - "rethinking imagenet pre training"
    - "direct uncertainty prediction for medical second opinions"
    - "do better imagenet models transfer better"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "automated enriched medical concept generation for chest x ray images"):
  with members:
    - "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"
    - "on the automatic generation of medical imaging reports"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a self attention based deep learning method for lesion attribute detection from ct reports"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "attention is all you need"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "hemis hetero modal image segmentation"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "low shot visual object recognition"
    - "low shot visual recognition by shrinking and hallucinating features"

Connected through hyperedge HE-5 (induced by source paper "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"):
  with members:
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-6 (induced by source paper "deep learning convolutional networks for multiphoton microscopy vasculature segmentation"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "theano a python framework for fast computation of mathematical expressions"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "sparse convolutional networks using the permutohedral lattice"
    - "an iterative convolutional neural network algorithm improves electron microscopy image segmentation"

Connected through hyperedge HE-7 (induced by source paper "unsupervised category discovery via looped deep pseudo task optimization using a large scale radiology image database"):
  with members:
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "webly supervised learning of convolutional networks"
    - "deep filter banks for texture recognition description and segmentation"
    - "simultaneous deep transfer across domains and tasks"
    - "unsupervised learning of visual representations using videos"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "automatic concept discovery from parallel text and visual corpora"

Connected through hyperedge HE-8 (induced by source paper "learning to read chest x rays recurrent neural cascade model for automated image annotation"):
  with members:
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "gated feedback recurrent neural networks"
    - "visualizing and understanding recurrent networks"
    - "sequence to sequence video to text"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "unifying visual semantic embeddings with multimodal neural language models"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)