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 "core decomposition in multilayer networks theory algorithms and applications"):
  Abstract: Multilayer networks are a powerful paradigm to model complex systems, where various relations might occur among the same set of entities. Despite the keen interest in a variety of problems, algorithms, and analysis methods in this type of network, the problem of extracting dense subgraphs has remained largely unexplored. #R##N#As a first step in this direction, we study the problem of core decomposition of a multilayer network. Unlike the single-layer counterpart in which cores are all nested into one another, in the multilayer context no total order exists among multilayer cores: they form a lattice whose size is exponential in the number of layers. In this setting we devise three algorithms which differ in the way they visit the core lattice and in their pruning techniques. We assess time and space efficiency of the three algorithms on a large variety of real-world multilayer networks. #R##N#We then study the problem of extracting only the inner-most cores, i.e., the cores that are not dominated by any other core in terms of their index on all the layers. As inner-most cores are orders of magnitude less than all the cores, it is desirable to develop algorithms that effectively exploit the maximality property and extract inner-most cores directly, without first computing a complete decomposition. #R##N#Moreover, we showcase an application of the multilayer core-decomposition tool to the problem of densest-subgraph extraction from multilayer networks. We introduce a definition of multilayer densest subgraph that trades-off between high density and number of layers in which the high density holds, and show how multilayer core decomposition can be exploited to approximate this problem with quality guarantees. We also exploit multilayer core decomposition to speed-up the extraction of frequent cross-graph quasi-cliques and to generalize the community-search problem to the multilayer setting.

Includes members:
  Member "on finding dense common subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "mining maximal span cores from temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a hybrid approach to temporal pattern matching")
  Member "to be connected or not to be connected that is the minimum inefficiency subgraph problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "diversified coherent core search on multi layer graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of community search over big graphs")
  Member "best friends forever bff finding lasting dense subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries")
  Member "when engagement meets similarity efficient k r core computation on social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of community search over big graphs")
  Member "a fast order based approach for core maintenance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey of community search over big graphs")
  Member "i o efficient core graph decomposition at web scale":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "critical edge identification a k truss based model")

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)