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: "correlation clustering with constrained cluster sizes and extended weights bounds"
  Abstract: We consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges. Our contributions are twofold: First, we introduce the problem of correlation clustering with bounded cluster sizes. Second, we extend the regime of weight values for which the clustering may be performed with constant approximation guarantees in polynomial time and apply the results to the bounded cluster size problem.

Connected through hyperedge HE-1 (induced by source paper "discovering polarized communities in signed networks"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "controversy and sentiment in online news"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "the slashdot zoo mining a social network with negative edges"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-2 (induced by source paper "a parallel projection method for metric constrained optimization"):
  with members:
    - "a projection method for metric constrained optimization"
    - "correlation clustering and biclustering with locally bounded errors"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"

Connected through hyperedge HE-3 (induced by source paper "correlation clustering generalized"):
  with members:
    - "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
    - "higher order organization of complex networks"
    - "motif clustering and overlapping clustering for social network analysis"

Connected through hyperedge HE-4 (induced by source paper "a new correlation clustering method for cancer mutation analysis"):
  with members:
    - "an approximate efficient solver for lp rounding"

Connected through hyperedge HE-5 (induced by source paper "parallel correlation clustering on big graphs"):
  with members:
    - "correlation clustering with noisy partial information"
    - "optimistic concurrency control for distributed unsupervised learning"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "correlation clustering with a fixed number of clusters"

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.LG(Machine Learning)