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: "cluster editing kernelization based on edge cuts"
  Abstract: Kernelization algorithms for the cluster editing problem have been a popular topic in the recent research in parameterized computation. Thus far most kernelization algorithms for this problem are based on the concept of critical cliques. In this paper, we present new observations and new techniques for the study of kernelization algorithms for the cluster editing problem. Our techniques are based on the study of the relationship between cluster editing and graph edge-cuts. As an application, we present an \({\cal O}(n^2)\)-time algorithm that constructs a 2k kernel for the weighted version of the cluster editing problem. Our result meets the best kernel size for the unweighted version for the cluster editing problem, and significantly improves the previous best kernel of quadratic size for the weighted version of the problem.

Connected through hyperedge HE-1 (induced by source paper "clustering to given connectivities"):
  with members:
    - "reducing cmso model checking to highly connected graphs"
    - "parameterized algorithms for partitioning graphs into highly connected clusters"
    - "clustering with local restrictions"
    - "finding connected secluded subgraphs"
    - "spanning circuits in regular matroids"
    - "fully polynomial time parameterized computations for graphs and matrices of low treewidth"
    - "parameterized algorithms for min max multiway cut and list digraph homomorphism"
    - "a short survey on data clustering algorithms"

Connected through hyperedge HE-2 (induced by source paper "on the complexity of multi parameterized cluster editing"):
  with members:
    - "clustering with local restrictions"
    - "clustering is difficult only when it does not matter"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "approximate association via dissociation"):
  with members:
    - "improved approximation algorithms for hitting 3 vertex paths"

Connected through hyperedge HE-4 (induced by source paper "chordal editing is fixed parameter tractable"):
  with members:
    - "on feedback vertex set new measure and new structures"
    - "unit interval editing is fixed parameter tractable"
    - "linear recognition of almost interval graphs"
    - "interval deletion is fixed parameter tractable"
    - "finding small separators in linear time via treewidth reduction"
    - "subexponential parameterized algorithm for minimum fill in"

Connected through hyperedge HE-5 (induced by source paper "a hybrid graph representation for exact graph algorithms"):
  with members:
    - "design by measure and conquer a faster exact algorithm for dominating set"

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)