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 "total variation based community detection using a nonlinear optimization approach"):
  Abstract: Maximizing the modularity of a network is a successful tool to identify an important community of nodes. However, this combinatorial optimization problem is known to be NP-hard. Inspired by recent nonlinear modularity eigenvector approaches, we introduce the modularity total variation $TV_Q$ and show that its box-constrained global maximum coincides with the maximum of the original discrete modularity function. Thus we describe a new nonlinear optimization approach to solve the equivalent problem leading to a community detection strategy based on $TV_Q$. The proposed approach relies on the use of a fast first-order method that embeds a tailored active-set strategy. We report extensive numerical comparisons with standard matrix-based approaches and the Generalized Ratio DCA approach for nonlinear modularity eigenvectors, showing that our new method compares favourably with state-of-the-art alternatives. Our software is available upon request.

Includes members:
  Member "community detection in networks via nonlinear modularity eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic block models are a discrete surface tension")
  Member "a nonlinear spectral method for core periphery detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlocal pagerank")
  Member "community detection in political twitter networks using nonnegative matrix factorization methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on computational politics")
  Member "a method based on total variation for network modularity optimization using the mbo scheme":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic block models are a discrete surface tension")
  Member "advanced coarsening schemes for graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "learning with submodular functions a convex optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online non monotone dr submodular maximization")
  Member "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when labelled data hurts deep semi supervised classification with the graph 1 laplacian")

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)