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: "deep structured scene parsing by learning with image descriptions"
  Abstract: This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations) that finely accords with human perception. We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixelwise object labeling and ii) a recursive neural network (RNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative user annotations (e.g., manually labeling semantic maps and relations), we train our deep model in a weakly-supervised manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and facilitate these trees discovering the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments suggest that our model is capable of producing meaningful and structured scene configurations and achieving more favorable scene labeling performance on PASCAL VOC 2012 over other state-of-the-art weakly-supervised methods.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-2 (induced by source paper "learning deep representations for semantic image parsing a comprehensive overview"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "progressively diffused networks for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "knowledge guided recurrent neural network learning for task oriented action prediction"
    - "interpretable structure evolving lstm"
    - "label refinement network for coarse to fine semantic segmentation"
    - "instance level salient object segmentation"

Connected through hyperedge HE-3 (induced by source paper "deep co space sample mining across feature transformation for semi supervised learning"):
  with members:
    - "multi view intact space learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "energy based generative adversarial network"
    - "semi supervised classification with graph convolutional networks"
    - "visualizing large scale and high dimensional data"
    - "revisiting semi supervised learning with graph embeddings"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-4 (induced by source paper "fully convolutional network for melanoma diagnostics"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "learning to segment breast biopsy whole slide images"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "cutting out the middleman measuring nuclear area in histopathology slides without segmentation"
    - "a survey of semantic segmentation"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-5 (induced by source paper "improving the expressiveness of deep learning frameworks with recursion"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep learning with dynamic computation graphs"
    - "a fast unified model for parsing and sentence understanding"
    - "theano a python framework for fast computation of mathematical expressions"
    - "hierarchical multiscale recurrent neural networks"
    - "tensorflow a system for large scale machine learning"
    - "deep hierarchical parsing for semantic segmentation"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-6 (induced by source paper "hierarchical scene parsing by weakly supervised learning with image descriptions"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "deep hierarchical parsing for semantic segmentation"
    - "holistically nested edge detection"
    - "instance aware semantic segmentation via multi task network cascades"
    - "conditional random fields as recurrent neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "constrained convolutional neural networks for weakly supervised segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "human pose estimation from depth images via inference embedded multi task learning"):
  with members:
    - "cost effective active learning for deep image classification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "dari distance metric and representation integration for person verification"
    - "geometric scene parsing with hierarchical lstm"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "structured feature learning for pose estimation"
    - "semantic object parsing with local global long short term memory"
    - "deep human parsing with active template regression"

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)