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: "gemsec graph embedding with self clustering"
  Abstract: Modern graph embedding procedures can efficiently extract features of nodes from graphs with millions of nodes. The features are later used as inputs for downstream predictive tasks. In this paper we propose GEMSEC a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their features. The procedure places nodes in an abstract feature space where the vertex features minimize the negative log likelihood of preserving sampled vertex neighborhoods, while the nodes are clustered into a fixed number of groups in this space. GEMSEC is a general extension of earlier work in the domain as it is an augmentation of the core optimization problem of sequence based graph embedding procedures and is agnostic of the neighborhood sampling strategy. We show that GEMSEC extracts high quality clusters on real world social networks and is competitive with other community detection algorithms. We demonstrate that the clustering constraint has a positive effect on representation quality and also that our procedure learns to embed and cluster graphs jointly in a robust and scalable manner.

Connected through hyperedge HE-1 (induced by source paper "joint inference on truth rumor and their sources in social networks"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "automatic stance detection using end to end memory networks"
    - "necessary and sufficient budgets in information source finding with querying adaptivity gap"
    - "on measuring bias in online information"
    - "fake news detection on social media a data mining perspective"
    - "rumor source detection under querying with untruthful answers"
    - "csi a hybrid deep model for fake news detection"
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-2 (induced by source paper "fast evaluation of link prediction by random sampling of unobserved links"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "link prediction based on graph neural networks"
    - "link prediction in dynamic graphs for recommendation"
    - "multi task graph autoencoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "performance of local information based link prediction a sampling perspective"

Connected through hyperedge HE-3 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks"):
  with members:
    - "simulating sir processes on networks using weighted shortest paths"
    - "adaptive influence maximization in dynamic social networks"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "effector detection in social networks"
    - "networkit a tool suite for large scale complex network analysis"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for task driven discovery of incomplete networks"):
  with members:
    - "partitioning graphs for the cloud using reinforcement learning"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep learning on graphs a survey"
    - "selective harvesting over networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "emergence of locomotion behaviours in rich environments"
    - "block models and personalized pagerank"

Connected through hyperedge HE-5 (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"
    - "communitygan community detection with generative adversarial nets"
    - "verse versatile graph embeddings from similarity measures"
    - "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-6 (induced by source paper "self falsifiable hierarchical detection of overlapping communities on social networks"):
  with members:
    - "hidden community detection in social networks"
    - "the ground truth about metadata and community detection in networks"
    - "community detection in networks with unequal groups"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "spectral redemption in clustering sparse networks"
    - "demon a local first discovery method for overlapping communities"
    - "percolation computation in complex networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "adversarial robustness of similarity based link prediction"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "hiding individuals and communities in a social network"
    - "snap a general purpose network analysis and graph mining library"
    - "semi supervised classification with graph convolutional networks"
    - "deepwalk online learning of social representations"
    - "link prediction in social networks the state of the art"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-8 (induced by source paper "generative temporal link prediction via self tokenized sequence modeling"):
  with members:
    - "computing maximal cliques in link streams"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"
    - "sequence to sequence learning with neural networks"

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.SI(Social and Information Networks)