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 "correlation clustering generalized"):
  Abstract: We present new results for LambdaCC and MotifCC, two recently introduced variants of the well-studied correlation clustering problem. Both variants are motivated by applications to network analysis and community detection, and have non-trivial approximation algorithms. We first show that the standard linear programming relaxation of LambdaCC has a $\Theta(\log n)$ integrality gap for a certain choice of the parameter $\lambda$. This sheds light on previous challenges encountered in obtaining parameter-independent approximation results for LambdaCC. We generalize a previous constant-factor algorithm to provide the best results, from the LP-rounding approach, for an extended range of $\lambda$. MotifCC generalizes correlation clustering to the hypergraph setting. In the case of hyperedges of degree $3$ with weights satisfying probability constraints, we improve the best approximation factor from $9$ to $8$. We show that in general our algorithm gives a $4(k-1)$ approximation when hyperedges have maximum degree $k$ and probability weights. We additionally present approximation results for LambdaCC and MotifCC where we restrict to forming only two clusters.

Includes members:
  Member "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic block models are a discrete surface tension")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "motif clustering and overlapping clustering for social network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "correlation clustering with constrained cluster sizes and extended weights bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discovering polarized communities in signed networks")

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.CC(Computational Complexity)