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 "modularity deltas a semi global centrality measure for complex networks"):
  Abstract: The phenomena of community structure in real-world networks has been central to network science's success. While much attention has been paid to community detection algorithms and evaluation criteria, a related question has been overlooked: what would the change in modularity be if a node were to be deleted from a network? In this work, we first show that the answer to this question can be calculated for all nodes in the network in $O(m)$ time. Then, we show that this quantity has implications in three research areas within network science: community analysis, network robustness, and community deception. Modularity-deltas are useful in community analysis as they are a semi-global measure, scalable like local measures but using global information through the network's this http URL robustness, modularity maximizing attack strategies are more efficient than betweenness-based ones, and are shown to damage the US Powergrid and European Road networks more effectively than degree based attacks. Finally, we show that inverting the robustness method to select modularity minimizing nodes solves the community deception problem in a scalable manner.

Includes members:
  Member "from louvain to leiden guaranteeing well connected communities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shapevis high dimensional data visualization at scale")
  Member "ga based q attack on community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")

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)