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: "parallel graph partitioning for complex networks"
  Abstract: Processing large complex networks like social networks or web graphs has recently attracted considerable interest. In order to do this in parallel, we need to partition them into pieces of about equal size. Unfortunately, previous parallel graph partitioners originally developed for more regular mesh-like networks do not work well for these networks. This paper addresses this problem by parallelizing and adapting the label propagation technique originally developed for graph clustering. By introducing size constraints, label propagation becomes applicable for both the coarsening and the refinement phase of multilevel graph partitioning. We obtain very high quality by applying a highly parallel evolutionary algorithm to the coarsened graph. The resulting system is both more scalable and achieves higher quality than state-of-the-art systems like ParMetis or PT-Scotch. For large complex networks the performance differences are very big. For example, our algorithm can partition a web graph with 3.3 billion edges in less than sixteen seconds using 512 cores of a high performance cluster while producing a high quality partition -- none of the competing systems can handle this graph on our system.

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 "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"

Connected through hyperedge HE-3 (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-4 (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"
    - "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-5 (induced by source paper "a partition centric distributed algorithm for identifying euler circuits in large graphs"):
  with members:
    - "a meta graph approach to analyze subgraph centric distributed programming models"
    - "gunrock a high performance graph processing library on the gpu"
    - "goffish a sub graph centric framework for large scale graph analytics"

Connected through hyperedge HE-6 (induced by source paper "cosine compressive network embedding on large scale information networks"):
  with members:
    - "mile a multi level framework for scalable graph embedding"
    - "word2bits quantized word vectors"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "compressing word embeddings via deep compositional code learning"
    - "natural language processing with small feed forward networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "faster support vector machines"):
  with members:
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "engineering multilevel support vector machines"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "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 "balanced k means for parallel geometric partitioning"):
  with members:
    - "communication free massively distributed graph generation"
    - "lightweight mpi communicators with applications to perfectly balanced quicksort"
    - "partitioning trillion edge graphs in minutes"
    - "recent advances in graph partitioning"
    - "engineering a scalable high quality graph partitioner"

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.DC(Distributed, Parallel, and Cluster Computing)