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: "probabilistic label relation graphs with ising models"
  Abstract: We consider classification problems in which the label space has structure. A common example is hierarchical label spaces, corresponding to the case where one label subsumes another (e.g., animal subsumes dog). But labels can also be mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy and exclusion) graph was introduced in [7]. This combined a conditional random field (CRF) with a deep neural network (DNN), resulting in state of the art results when applied to visual object classification problems where the training labels were drawn from different levels of the ImageNet hierarchy (e.g., an image might be labeled with the basic level category "dog", rather than the more specific label "husky"). In this paper, we extend the HEX model to allow for soft or probabilistic relations between labels, which is useful when there is uncertainty about the relationship between two labels (e.g., an antelope is "sort of" furry, but not to the same degree as a grizzly bear). We call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can be converted to an Ising model, which allows us to use existing off-the-shelf inference methods (in contrast to the HEX method, which needed specialized inference algorithms). Experimental results show significant improvements in a number of large-scale visual object classification tasks, outperforming the previous HEX model.

Connected through hyperedge HE-1 (induced by source paper "structured label inference for visual understanding"):
  with members:
    - "learning structured inference neural networks with label relations"
    - "deep residual learning for image recognition"
    - "every moment counts dense detailed labeling of actions in complex videos"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "imagenet large scale visual recognition challenge"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-2 (induced by source paper "multi label zero shot learning with structured knowledge graphs"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "gated graph sequence neural networks"
    - "fast zero shot image tagging"
    - "synthesized classifiers for zero shot learning"
    - "the more you know using knowledge graphs for image classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "predicting visual exemplars of unseen classes for zero shot learning"
    - "multi instance visual semantic embedding"

Connected through hyperedge HE-3 (induced by source paper "hierarchical label inference for video classification"):
  with members:
    - "youtube 8m a large scale video classification benchmark"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"

Connected through hyperedge HE-4 (induced by source paper "learning structured inference neural networks with label relations"):
  with members:
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "fully connected deep structured networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-5 (induced by source paper "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"):
  with members:
    - "deep structured models for group activity recognition"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "joint inference of groups events and human roles in aerial videos"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"

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.LG(Machine Learning)