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: "patch based convolutional neural network for whole slide tissue image classification"
  Abstract: Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN.

Connected through hyperedge HE-1 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field"):
  with members:
    - "a deep learning algorithm for one step contour aware nuclei segmentation of histopathological images"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "defraudnet end2end fingerprint spoof detection using patch level attention"):
  with members:
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "cbam convolutional block attention module"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "residual attention network for image classification"
    - "fingerprint spoof buster"
    - "densely connected convolutional networks"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

Connected through hyperedge HE-3 (induced by source paper "sos selective objective switch for rapid immunofluorescence whole slide image classification"):
  with members:
    - "attentive single tasking of multiple tasks"
    - "improving whole slide segmentation through visual context a systematic study"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "on calibration of modern neural networks"
    - "an analysis of scale invariance in object detection snip"
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-4 (induced by source paper "multi scale domain adversarial multiple instance cnn for cancer subtype classification with non annotated histopathological images"):
  with members:
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "attention based deep multiple instance learning"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "multiple instance learning for heterogeneous images training a cnn for histopathology"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "analysis of multi field of view cnn and attention cnn on h e stained whole slide images on hepatocellular carcinoma"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (induced by source paper "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "videos as space time region graphs"
    - "step by step erasion one by one collection a weakly supervised temporal action detector"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal segment networks for action recognition in videos"
    - "multiple instance learning a survey of problem characteristics and applications"

Connected through hyperedge HE-8 (induced by source paper "a comprehensive analysis of weakly supervised semantic segmentation in different image domains"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense fusion classmate network for land cover classification"
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "constrained cnn losses for weakly supervised segmentation"
    - "weakly supervised semantic segmentation of satellite images"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"

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)