human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "network clustering via maximizing modularity approximation algorithms and theoretical limits"
  Abstract: Many social networks and complex systems are found to be naturally divided into clusters of densely connected nodes, known as community structure (CS). Finding CS is one of fundamental yet challenging topics in network science. One of the most popular classes of methods for this problem is to maximize Newman's modularity. However, there is a littleunderstood on how well we can approximate the maximum modularity as well as the implications of finding community structure with provable guarantees. In this paper, we settle definitely the approximability of modularity clustering, proving that approximating the problem within any (multiplicative) positive factor is intractable, unless P = NP. Yet we propose the first additive approximation algorithm for modularity clustering with a constant factor. Moreover, we providea rigorous proof that a CS with modularity arbitrary close to maximum modularity QOPT might bear no similarity to the optimal CS of maximum modularity. Thus even when CS with near-optimal modularity are found, other verification methods are needed to confirm the significance of the structure.

Connected through hyperedge HE-1 (induced by source paper "moments of uniform random multigraphs with fixed degree sequences"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "graphic realizations of joint degree matrices"
    - "the switch markov chain for sampling irregular graphs"

Connected through hyperedge HE-2 (induced by source paper "graph clustering in all parameter regimes"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "learning resolution parameters for graph clustering"
    - "correlation clustering generalized"
    - "multiresolution consensus clustering in networks"

Connected through hyperedge HE-3 (induced by source paper "a projection method for metric constrained optimization"):
  with members:
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-4 (induced by source paper "indirect influences links ranking and deconstruction of networks"):
  with members:
    - "on the stability of the pwp method"
    - "clustering and community detection in directed networks a survey"

Connected through hyperedge HE-5 (induced by source paper "the parameterised complexity of computing the maximum modularity of a graph"):
  with members:
    - "additive approximation algorithms for modularity maximization"
    - "parameterized integer quadratic programming variables and coefficients"

Connected through hyperedge HE-6 (induced by source paper "additive approximation algorithms for modularity maximization"):
  with members:
    - "maximizing barber s bipartite modularity is also hard"

Question: Please tell me which class the center node 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)