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: "shared memory graph truss decomposition"
  Abstract: We present PKT, a new shared-memory parallel algorithm and OpenMP implementation for the truss decomposition of large sparse graphs. A k-truss is a dense subgraph definition that can be considered a relaxation of a clique. Truss decomposition refers to a partitioning of all the edges in the graph based on their k-truss membership. The truss decomposition of a graph has many applications. We show that our new approach PKT consistently outperforms other truss decomposition approaches for a collection of large sparse graphs and on a 24-core shared-memory server. PKT is based on a recently proposed algorithm for k-core decomposition.

Connected through hyperedge HE-1 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs"):
  with members:
    - "enumerating top k quasi cliques"
    - "efficiently enumerating all maximal cliques with bit parallelism"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "incremental maintenance of maximal cliques in a dynamic graph"
    - "enumerating maximal bicliques from a large graph using mapreduce"
    - "listing all maximal cliques in large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "local algorithms for hierarchical dense subgraph discovery"):
  with members:
    - "estimation of graphlet statistics"
    - "static graph challenge subgraph isomorphism"
    - "higher order organization of complex networks"
    - "locally estimating core numbers"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "parallel peeling algorithms"
    - "graph cluster randomization network exposure to multiple universes"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-3 (induced by source paper "shared memory parallel maximal clique enumeration"):
  with members:
    - "local algorithms for hierarchical dense subgraph discovery"
    - "change sensitive algorithms for maintaining maximal cliques in a dynamic graph"
    - "enumerating maximal bicliques from a large graph using mapreduce"
    - "listing all maximal cliques in large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

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)