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: "learning transferrable knowledge for semantic segmentation with deep convolutional neural network"
  Abstract: We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class labels. To make the segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. In this architecture, the model generates spatial highlights of each category presented in an image using an attention model, and subsequently generates foreground segmentation for each highlighted region using decoder. Combining attention model, we show that the decoder trained with segmentation annotations in different categories can boost the performance of weakly-supervised semantic segmentation. The proposed algorithm demonstrates substantially improved performance compared to the state-of-the-art weakly-supervised techniques in challenging PASCAL VOC 2012 dataset when our model is trained with the annotations in 60 exclusive categories in Microsoft COCO dataset.

Connected through hyperedge HE-1 (induced by source paper "driver drowsiness detection using condition adaptive representation learning framework"):
  with members:
    - "sparse 3d convolutional neural networks for large scale shape retrieval"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "dynamic texture and scene classification by transferring deep image features"
    - "multi view face detection using deep convolutional neural networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (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-3 (induced by source paper "deconvolutional feature stacking for weakly supervised semantic segmentation"):
  with members:
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "learning to segment object candidates"
    - "instance aware semantic segmentation via multi task network cascades"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "decoupled deep neural network for semi supervised semantic segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-4 (induced by source paper "seed expand and constrain three principles for weakly supervised image segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "improving weakly supervised object localization by micro annotation"
    - "deconvolutional feature stacking for weakly supervised semantic segmentation"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "learning deep features for discriminative localization"
    - "what s the point semantic segmentation with point supervision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "abcnn attention based convolutional neural network for modeling sentence pairs"):
  with members:
    - "a neural attention model for abstractive sentence summarization"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "applying deep learning to answer selection a study and an open task"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "convolutional neural network architectures for matching natural language sentences"
    - "abc cnn an attention based convolutional neural network for visual question answering"
    - "reasoning about entailment with neural attention"

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)