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: "communitygan community detection with generative adversarial nets"
  Abstract: Community detection refers to the task of discovering groups of vertices sharing similar properties or functions so as to understand the network data. With the recent development of deep learning, graph representation learning techniques are also utilized for community detection. However, the communities can only be inferred by applying clustering algorithms based on learned vertex embeddings. These general cluster algorithms like K-means and Gaussian Mixture Model cannot output much overlapped communities, which have been proved to be very common in many real-world networks. In this paper, we propose CommunityGAN, a novel community detection framework that jointly solves overlapping community detection and graph representation learning. First, unlike the embedding of conventional graph representation learning algorithms where the vector entry values have no specific meanings, the embedding of CommunityGAN indicates the membership strength of vertices to communities. Second, a specifically designed Generative Adversarial Net (GAN) is adopted to optimize such embedding. Through the minimax competition between the motif-level generator and discriminator, both of them can alternatively and iteratively boost their performance and finally output a better community structure. Extensive experiments on synthetic data and real-world tasks demonstrate that CommunityGAN achieves substantial community detection performance gains over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "sampled in pairs and driven by text a new graph embedding framework"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "representation learning on graphs methods and applications"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "node2vec scalable feature learning for networks"
    - "fully character level neural machine translation without explicit segmentation"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "vgraph a generative model for joint community detection and node representation learning"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "verse versatile graph embeddings from similarity measures"
    - "gemsec graph embedding with self clustering"
    - "a unified framework for community detection and network representation learning"
    - "node2vec scalable feature learning for networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "on geometric structure of activation spaces in neural networks"):
  with members:
    - "on generalization error bounds of noisy gradient methods for non convex learning"
    - "acekg a large scale knowledge graph for academic data mining"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "curvature based comparison of two neural networks"
    - "computing the approximate convex hull in high dimensions"
    - "deep learning in neural networks"
    - "deepwalk online learning of social representations"
    - "approximating the volume of unions and intersections of high dimensional geometric objects"

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.AI(Artificial Intelligence)