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: "memetic graph clustering"
  Abstract: It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. We instantiate our scheme with local search for modularity and show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation~challenge under consideration using a small amount of time.

Connected through hyperedge HE-1 (induced by source paper "multilevel acyclic hypergraph partitioning"):
  with members:
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "scalable edge partitioning"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "high quality shared memory graph partitioning"
    - "partitioning trillion edge graphs in minutes"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "advanced multilevel node separator algorithms"

Connected through hyperedge HE-2 (induced by source paper "scalable graph algorithms"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "practical minimum cut algorithms"
    - "scalable edge partitioning"
    - "shared memory exact minimum cuts"
    - "scalable kernelization for maximum independent sets"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-3 (induced by source paper "recent progress on graph partitioning problems using evolutionary computation"):
  with members:
    - "memetic multilevel hypergraph partitioning"
    - "recent advances in graph partitioning"
    - "distributed evolutionary graph partitioning"

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.NE(Neural and Evolutionary Computing)