human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "shared memory parallel maximal clique enumeration"):
  Abstract: We present shared-memory parallel methods for Maximal Clique Enumeration (MCE) from a graph. MCE is a fundamental and well-studied graph analytics task, and is a widely used primitive for identifying dense structures in a graph. Due to its computationally intensive nature, parallel methods are imperative for dealing with large graphs. However, surprisingly, there do not yet exist scalable and parallel methods for MCE on a shared-memory parallel machine. In this work, we present efficient shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing that they can scale to a large number of processors, and (3) our implementations on a multicore machine shows a good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE.

Includes members:
  Member "local algorithms for hierarchical dense subgraph discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "shared memory graph truss decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "change sensitive algorithms for maintaining maximal cliques in a dynamic graph":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "enumerating top k quasi cliques")
  Member "enumerating maximal bicliques from a large graph using mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "listing all maximal cliques in large sparse real world graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "listing all maximal cliques in sparse graphs in near optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "teaser fast and certifiable point cloud registration")

Question: Please tell me which class the source paper 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)