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 and biclustering with locally bounded errors"
  Abstract: We consider a generalized version of the correlation clustering problem, defined as follows. Given a complete graph $G$ whose edges are labeled with $+$ or $-$, we wish to partition the graph into clusters while trying to avoid errors: $+$ edges between clusters or $-$ edges within clusters. Classically, one seeks to minimize the total number of such errors. We introduce a new framework that allows the objective to be a more general function of the number of errors at each vertex (for example, we may wish to minimize the number of errors at the worst vertex) and provide a rounding algorithm which converts "fractional clusterings" into discrete clusterings while causing only a constant-factor blowup in the number of errors at each vertex. This rounding algorithm yields constant-factor approximation algorithms for the discrete problem under a wide variety of objective functions.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs"):
  with members:
    - "hypergraph cuts with general splitting functions"
    - "clustering in graphs and hypergraphs with categorical edge labels"
    - "learning resolution parameters for graph clustering"
    - "a parallel projection method for metric constrained optimization"
    - "hypergraph clustering with categorical edge labels"
    - "higher order clustering in networks"
    - "motif and hypergraph correlation clustering"
    - "correlation clustering generalized"

Connected through hyperedge HE-3 (induced by source paper "a parallel projection method for metric constrained optimization"):
  with members:
    - "a projection method for metric constrained optimization"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"

Connected through hyperedge HE-4 (induced by source paper "min max correlation clustering via multicut"):
  with members:
    - "local guarantees in graph cuts and clustering"
    - "instancecut from edges to instances with multicut"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"

Connected through hyperedge HE-5 (induced by source paper "complexity of modification problems for reciprocal best match graphs"):
  with members:
    - "phylogenomics with paralogs"
    - "fast biclustering by dual parameterization"

Connected through hyperedge HE-6 (induced by source paper "improved algorithms for correlation clustering with local objectives"):
  with members:
    - "local guarantees in graph cuts and clustering"

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.DS(Data Structures and Algorithms)