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 "local algorithms for hierarchical dense subgraph discovery"):
  Abstract: Finding the dense regions of a graph and relations among them is a fundamental problem in network analysis. Core and truss decompositions reveal dense subgraphs with hierarchical relations. The incremental nature of algorithms for computing these decompositions and the need for global information at each step of the algorithm hinders scalable parallelization and approximations since the densest regions are not revealed until the end. In a previous work, Lu et al. proposed to iteratively compute the $h$-indices of neighbor vertex degrees to obtain the core numbers and prove that the convergence is obtained after a finite number of iterations. This work generalizes the iterative $h$-index computation for truss decomposition as well as nucleus decomposition which leverages higher-order structures to generalize core and truss decompositions. In addition, we prove convergence bounds on the number of iterations. We present a framework of local algorithms to obtain the core, truss, and nucleus decompositions. Our algorithms are local, parallel, offer high scalability, and enable approximations to explore time and quality trade-offs. Our shared-memory implementation verifies the efficiency, scalability, and effectiveness of our local algorithms on real-world networks.

Includes members:
  Member "shared memory graph truss decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "estimation of graphlet statistics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets")
  Member "static graph challenge subgraph isomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "locally estimating core numbers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using core periphery structure to predict high centrality nodes in time varying networks")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the power of pivoting for exact clique counting")
  Member "parallel peeling algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust set reconciliation via locality sensitive hashing")
  Member "graph cluster randomization network exposure to multiple universes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "inferring individual level causal models from graph based relational time series")

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.SI(Social and Information Networks)