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 "new methods for incorporating network cyclic structures to improve community detection"):
  Abstract: A distinguishing property of communities in networks is that cycles are more prevalent within communities than across communities. Hence, the detection of these communities may be aided through the use of measures of the local "richness" of cyclic structures. We investigate the use of two methods for quantifying this richness---loop modulus (LM) and renewal non-backtracking random walks (RNBRW)---to improve the performance of existing community detection algorithms. LM solves a quadratic program to find an optimal allocation of edge usage across cycles to minimize cycle overlap, thereby giving a rigorous way to quantify the importance of edges. RNBRW, introduced for the first time in this paper, quantifies edge importance as the likelihood of an edge completing a cycle in a non-backtracking random walk. We argue that RNBRW provides an efficient and scalable alternative to LM. We give simulation results that suggest pre-weighting edges by the proposed methods can improve the performance of popular community detection algorithms substantially. Our methods are especially efficient for the challenging case of detecting communities in sparse graphs.

Includes members:
  Member "network clustering and community detection using modulus of families of loops":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new method for quantifying network cyclic structure to improve community detection")
  Member "equivalence between modularity optimization and maximum likelihood methods for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mga momentum gradient attack on network")
  Member "using triangles to improve community detection in directed networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "enhancing community detection using a network weighting strategy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)