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 "graph clustering via qubo and digital annealing"):
  Abstract: This article empirically examines the computational cost of solving a known hard problem, graph clustering, using novel purpose-built computer hardware. We express the graph clustering problem as an intra-cluster distance or dissimilarity minimization problem. We formulate our poblem as a quadratic unconstrained binary optimization problem and employ a novel computer architecture to obtain a numerical solution. Our starting point is a clustering formulation from the literature. This formulation is then converted to a quadratic unconstrained binary optimization formulation. Finally, we use a novel purpose-built computer architecture to obtain numerical solutions. For benchmarking purposes, we also compare computational performances to those obtained using a commercial solver, Gurobi, running on conventional hardware. Our initial results indicate the purpose-built hardware provides equivalent solutions to the commercial solver, but in a very small fraction of the time required.

Includes members:
  Member "using synthetic networks for parameter tuning in community detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance")
  Member "a statistical density based analysis of graph clustering algorithm performance":
    also connected through other hyperedges:
      (none)
  Member "testing graph clusterability algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "walking randomly massively and efficiently")
  Member "a tutorial on formulating and using qubo models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on modeling local search with special purpose combinatorial optimization hardware")
  Member "axioms for graph clustering quality functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a statistical density based analysis of graph clustering algorithm performance")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "a tutorial on spectral clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")

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)