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: "a survey of signed network mining in social media"
  Abstract: Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.

Connected through hyperedge HE-1 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "privacy in social media identification mitigation and applications"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "signed network modeling based on structural balance theory"
    - "semi supervised learning for detecting human trafficking"

Connected through hyperedge HE-2 (induced by source paper "deep network embedding for graph representation learning in signed networks"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "negative link prediction in social media"
    - "efficient estimation of word representations in vector space"
    - "sitting closer to friends than enemies revisited"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-3 (induced by source paper "finding large balanced subgraphs in signed networks"):
  with members:
    - "discovering polarized communities in signed networks"
    - "reducing controversy by connecting opposing views"
    - "controversy and sentiment in online news"
    - "maximum balanced subgraph problem parameterized above lower bound"

Connected through hyperedge HE-4 (induced by source paper "searching for polarization in signed graphs a local spectral approach"):
  with members:
    - "discovering polarized communities in signed networks"
    - "heat kernel based community detection"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"

Connected through hyperedge HE-5 (induced by source paper "a game generative network framework with its application to relationship inference"):
  with members:
    - "finding friend and foe in multi agent games"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-6 (induced by source paper "new a generic learning model for tie strength prediction in networks"):
  with members:
    - "detecting strong ties using network motifs"
    - "prediction of links and weights in networks by reliable routes"

Connected through hyperedge HE-7 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"

Connected through hyperedge HE-8 (induced by source paper "hyperbolic node embedding for signed networks"):
  with members:
    - "poincar e glove hyperbolic word embeddings"
    - "graph embedding techniques applications and performance a survey"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "sne signed network embedding"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "exploiting social network structure for person to person sentiment analysis"
    - "efficient estimation of word representations in vector space"

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)