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 markov random field for image modeling"
  Abstract: Markov Random Fields (MRFs), a formulation widely used in generative image modeling, have long been plagued by the lack of expressive power. This issue is primarily due to the fact that conventional MRFs formulations tend to use simplistic factors to capture local patterns. In this paper, we move beyond such limitations, and propose a novel MRF model that uses fully-connected neurons to express the complex interactions among pixels. Through theoretical analysis, we reveal an inherent connection between this model and recurrent neural networks, and thereon derive an approximated feed-forward network that couples multiple RNNs along opposite directions. This formulation combines the expressive power of deep neural networks and the cyclic dependency structure of MRF in a unified model, bringing the modeling capability to a new level. The feed-forward approximation also allows it to be efficiently learned from data. Experimental results on a variety of low-level vision tasks show notable improvement over state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "learning human object interactions by graph parsing neural networks"):
  with members:
    - "situation recognition with graph neural networks"
    - "detecting and recognizing human object interactions"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "predicting human activities using stochastic grammar"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "deformable convolutional networks"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-2 (induced by source paper "rethinking the form of latent states in image captioning"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "detecting visual relationships with deep relational networks"
    - "crnn a joint neural network for redundancy detection"
    - "contrastive learning for image captioning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "attention correctness in neural image captioning"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-3 (induced by source paper "from pixels to buildings end to end probabilistic deep networks for large scale semantic mapping"):
  with members:
    - "structured prediction using cgans with fusion discriminator"
    - "learning deep generative spatial models for mobile robots"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "on the latent variable interpretation in sum product networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "structured prediction energy networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-4 (induced by source paper "detecting visual relationships with deep relational networks"):
  with members:
    - "places an image database for deep scene understanding"
    - "spice semantic propositional image caption evaluation"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual question answering a survey of methods and datasets"
    - "visual relationship detection with language priors"
    - "from images to sentences through scene description graphs using commonsense reasoning and knowledge"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-5 (induced by source paper "cern confidence energy recurrent network for group activity recognition"):
  with members:
    - "semantic object parsing with graph lstm"
    - "hierarchical deep temporal models for group activity recognition"
    - "energy based generative adversarial network"
    - "structured prediction energy networks"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "conditional random fields as recurrent neural networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"

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)