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: "n level graph partitioning"
  Abstract: We present a multi-level graph partitioning algorithm based on the extreme idea to contract only a single edge on each level of the hierarchy. This obviates the need for a matching algorithm and promises very good partitioning quality since there are very few changes between two levels. Using an efficient data structure and new flexible ways to break local search improvements early, we obtain an algorithm that scales to large inputs and produces the best known partitioning results for many inputs. For example, in Walshaw's well known benchmark tables we achieve 155 improvements dominating the entries for large graphs.

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"
    - "k way hypergraph partitioning via n level recursive bisection"

Connected through hyperedge HE-2 (induced by source paper "evolutionary n level hypergraph partitioning with adaptive coarsening"):
  with members:
    - "memetic multilevel hypergraph partitioning"
    - "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"
    - "satzilla portfolio based algorithm selection for sat"

Connected through hyperedge HE-3 (induced by source paper "high quality shared memory graph partitioning"):
  with members:
    - "in place parallel super scalar samplesort ips 4 o"
    - "engineering parallel algorithms for community detection in massive networks"
    - "concurrent hash tables fast and general"
    - "parallel graph partitioning for complex networks"
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "the power of simple tabulation hashing"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-4 (induced by source paper "advanced multilevel node separator algorithms"):
  with members:
    - "graph bisection with pareto optimization"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "customizable contraction hierarchies"
    - "recent advances in graph partitioning"
    - "engineering multilevel graph partitioning algorithms"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-5 (induced by source paper "k way hypergraph partitioning via n level recursive bisection"):
  with members:
    - "n level hypergraph partitioning"
    - "partitioning complex networks via size constrained clustering"
    - "distributed evolutionary graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-6 (induced by source paper "n level hypergraph partitioning"):
  with members:
    - "partitioning complex networks via size constrained clustering"
    - "distributed evolutionary graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-7 (induced by source paper "tree based coarsening and partitioning of complex networks"):
  with members:
    - "partitioning complex networks via size constrained clustering"
    - "recent advances in graph partitioning"
    - "advanced coarsening schemes for graph partitioning"
    - "engineering a scalable high quality graph partitioner"
    - "graph sparsification by effective resistances"
    - "fast computation of small cuts via cycle space sampling"

Connected through hyperedge HE-8 (induced by source paper "recent advances in graph partitioning"):
  with members:
    - "on the parameterized complexity of computing balanced partitions in graphs"
    - "semi external algorithms for graph partitioning and clustering"
    - "tree based coarsening and partitioning of complex networks"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "parallel graph partitioning for complex networks"
    - "algorithms for mapping parallel processes onto grid and torus architectures"
    - "partitioning complex networks via size constrained clustering"
    - "community detection and 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.DS(Data Structures and Algorithms)