human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adversarial enhancement for community detection in complex networks"):
  Abstract: Community detection plays a significant role in network analysis. However, it also faces numerous challenges like adversarial attacks. How to further improve the performance and robustness of community detection for real-world networks has raised great concerns. In this paper, we propose a concept of adversarial enhancement for community detection, and present two adversarial enhancement algorithms: one is named adversarial enhancement via genetic algorithm (AE-GA), in which the modularity and the number of clusters are used to design a fitness function to solve the resolution limit problem; and the other is called adversarial enhancement via vertex similarity (AE-VS), integrating multiple information of community structures captured by diverse vertex similarities, which scales well on large-scale networks. The two algorithms are tested along with six existing community detection algorithms on four real-world networks. Comprehensive experimental results show that, by comparing with two traditional enhancement strategies, our methods help six community detection algorithms achieve more significant performance improvement. Moreover, experiments on the corresponding adversarial networks indicate that our methods can rebuild the network structure destroyed by adversarial attacks to certain extent, achieving stronger defense against community detection deception.

Includes members:
  Member "edmot an edge enhancement approach for motif aware community detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "adversarial attack on graph structured data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "adversarial attacks on node embeddings via graph poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "ga based q attack on community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "target defense against link prediction based attacks via evolutionary perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mga momentum gradient attack on network")
  Member "hiding individuals and communities in a social network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep graph matching consensus")
  Member "enhancing community detection using a network weighting strategy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an ensemble based on a bi objective evolutionary spectral algorithm for graph clustering")

Question: Please tell me which class the source paper 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)