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 multilevel hypergraph partitioning"
  Abstract: Hypergraph partitioning has a wide range of important applications such as VLSI design or scientific computing. With focus on solution quality, we develop the first multilevel memetic algorithm to tackle the problem. Key components of our contribution are new effective multilevel recombination and mutation operations that provide a large amount of diversity. We perform a wide range of experiments on a benchmark set containing instances from application areas such VLSI, SAT solving, social networks, and scientific computing. Our new algorithm computes the best result on almost all instances.

Connected through hyperedge HE-1 (induced by source paper "multilevel acyclic hypergraph partitioning"):
  with members:
    - "memetic graph clustering"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "scalable edge partitioning"
    - "graph partitioning with acyclicity constraints"
    - "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"
    - "memetic graph clustering"
    - "practical minimum cut algorithms"
    - "scalable edge partitioning"
    - "shared memory exact minimum cuts"
    - "scalable kernelization for maximum independent sets"
    - "graph partitioning with acyclicity constraints"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-3 (induced by source paper "hypergraph partitioning with embeddings"):
  with members:
    - "pytorch biggraph a large scale graph embedding system"
    - "aggregative coarsening for multilevel hypergraph partitioning"
    - "verse versatile graph embeddings from similarity measures"
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "k way hypergraph partitioning via n level recursive bisection"

Connected through hyperedge HE-4 (induced by source paper "partition hypergraphs with embeddings"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "aggregative coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "advanced coarsening schemes for graph partitioning"

Connected through hyperedge HE-5 (induced by source paper "evolutionary n level hypergraph partitioning with adaptive coarsening"):
  with members:
    - "k way hypergraph partitioning via n level recursive bisection"
    - "parallel graph partitioning for complex networks"
    - "algorithm selection for combinatorial search problems a survey"
    - "distributed evolutionary graph partitioning"
    - "n level graph partitioning"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-6 (induced by source paper "evaluation of a flow based hypergraph bipartitioning algorithm"):
  with members:
    - "network flow based refinement for multilevel hypergraph partitioning"
    - "graph bisection with pareto optimization"
    - "customizable contraction hierarchies"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-7 (induced by source paper "faster support vector machines"):
  with members:
    - "graph partitioning with acyclicity constraints"
    - "engineering multilevel support vector machines"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "parallel graph partitioning for complex networks"
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "parallel graph decompositions using random shifts"
    - "a divide and conquer solver for kernel support vector machines"

Connected through hyperedge HE-8 (induced by source paper "network flow based refinement for multilevel hypergraph partitioning"):
  with members:
    - "aggregative coarsening for multilevel hypergraph partitioning"
    - "hype massive hypergraph partitioning with neighborhood expansion"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "graph bisection with pareto optimization"
    - "partitioning complex networks via size constrained clustering"
    - "engineering multilevel graph partitioning algorithms"
    - "benchmarking optimization software with performance profiles"

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)