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: "engineering parallel algorithms for community detection in massive networks"
  Abstract: The amount of graph-structured data has recently experienced an enormous growth in many applications. To transform such data into useful information, fast analytics algorithms and software tools are necessary. One common graph analytics kernel is disjoint community detection (or graph clustering). Despite extensive research on heuristic solvers for this task, only few parallel codes exist, although parallelism will be necessary to scale to the data volume of real-world applications. We address the deficit in computing capability by a flexible and extensible community detection framework with shared-memory parallelism. Within this framework we design and implement efficient parallel community detection heuristics: A parallel label propagation scheme; the first large-scale parallelization of the well-known Louvain method, as well as an extension of the method adding refinement; and an ensemble scheme combining the above. In extensive experiments driven by the algorithm engineering paradigm, we identify the most successful parameters and combinations of these algorithms. We also compare our implementations with state-of-the-art competitors. The processing rate of our fastest algorithm often reaches 50 M edges/second. We recommend the parallel Louvain method and our variant with refinement as both qualitatively strong and fast. Our methods are suitable for massive data sets with billions of edges. (A preliminary version of this paper appeared in   Proceedings of the 42nd International Conference on Parallel Processing (ICPP 2013)   [35]  .)

Connected through hyperedge HE-1 (induced by source paper "high quality shared memory graph partitioning"):
  with members:
    - "in place parallel super scalar samplesort ips 4 o"
    - "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"
    - "n level graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-2 (induced by source paper "scaling up group closeness maximization"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "scalable betweenness centrality maximization via sampling"
    - "fast and simple computation of top k closeness centralities"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "networkit a tool suite for large scale complex network analysis"
    - "computing classic closeness centrality at scale"
    - "fast approximation of centrality"

Connected through hyperedge HE-3 (induced by source paper "distributed graph clustering using modularity and map equation"):
  with members:
    - "thrill high performance algorithmic distributed batch data processing with c"
    - "i o efficient generation of massive graphs following the lfr benchmark"

Connected through hyperedge HE-4 (induced by source paper "simple distributed graph clustering using modularity and map equation"):
  with members:
    - "thrill high performance algorithmic distributed batch data processing with c"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-5 (induced by source paper "generating realistic scaled complex networks"):
  with members:
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "generating scaled replicas of real world complex networks"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "tree based coarsening and partitioning of complex networks"
    - "recent advances in graph partitioning"

Connected through hyperedge HE-6 (induced by source paper "mathematical foundations of the graphblas"):
  with members:
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "approximating betweenness centrality in fully dynamic networks"
    - "gunrock a high performance graph processing library on the gpu"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "networkit a tool suite for large scale complex network analysis"
    - "parallel graph partitioning for complex networks"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-7 (induced by source paper "structure preserving sparsification methods for social networks"):
  with members:
    - "single and multi level network sparsification by algebraic distance"
    - "structure preserving sparsification of social networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "network sampling from static to streaming graphs"
    - "social structure of facebook networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-8 (induced by source paper "many to many correspondences between partitions introducing a cut based approach"):
  with members:
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "efficient minimization of decomposable submodular functions"
    - "bipartite graph partitioning and data clustering"
    - "objective criteria for the evaluation of clustering methods"

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)