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: "k way hypergraph partitioning via n level recursive bisection"
  Abstract: We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one at a time. Using several caching and lazy-evaluation techniques during coarsening and refinement, we reduce the running time by up to two-orders of magnitude compared to a naive $n$-level algorithm that would be adequate for ordinary graph partitioning. The overall performance is even better than the widely used hMetis hypergraph partitioner that uses a classical multilevel algorithm with few levels. Aided by a portfolio-based approach to initial partitioning and adaptive budgeting of imbalance within recursive bipartitioning, we achieve very high quality. We assembled a large benchmark set with 310 hypergraphs stemming from application areas such VLSI, SAT solving, social networks, and scientific computing. We achieve significantly smaller cuts than hMetis and PaToH, while being faster than hMetis. Considerably larger improvements are observed for some instance classes like social networks, for bipartitioning, and for partitions with an allowed imbalance of 10%. The algorithm presented in this work forms the basis of our hypergraph partitioning framework KaHyPar (Karlsruhe Hypergraph Partitioning).

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"
    - "memetic multilevel hypergraph partitioning"
    - "high quality shared memory graph partitioning"
    - "partitioning trillion edge graphs in minutes"
    - "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"
    - "memetic multilevel hypergraph partitioning"

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"
    - "memetic multilevel hypergraph partitioning"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

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"
    - "memetic multilevel hypergraph partitioning"
    - "node2vec scalable feature learning for networks"
    - "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:
    - "memetic multilevel hypergraph partitioning"
    - "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 "a comparison of partitioning strategies in ac optimal power flow"):
  with members:
    - "recent advances in graph partitioning"
    - "engineering multilevel graph partitioning algorithms"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-7 (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"
    - "memetic multilevel hypergraph partitioning"
    - "graph bisection with pareto optimization"
    - "partitioning complex networks via size constrained clustering"
    - "engineering multilevel graph partitioning algorithms"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-8 (induced by source paper "hype massive hypergraph partitioning with neighborhood expansion"):
  with members:
    - "mesh a flexible distributed hypergraph processing system"
    - "social hash partitioner a scalable distributed hypergraph partitioner"
    - "adwise adaptive window based streaming edge partitioning for high speed graph processing"
    - "spinner scalable graph partitioning in the cloud"

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)