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 "a sampling framework for counting temporal motifs"):
  Abstract: Pattern counting in graphs is fundamental to network science tasks, and there are many scalable methods for approximating counts of small patterns, often called motifs, in large graphs. However, modern graph datasets now contain richer structure, and incorporating temporal information in particular has become a critical part of network analysis. Temporal motifs, which are generalizations of small subgraph patterns that incorporate temporal ordering on edges, are an emerging part of the network analysis toolbox. However, there are no algorithms for fast estimation of temporal motifs counts; moreover, we show that even counting simple temporal star motifs is NP-complete. Thus, there is a need for fast and approximate algorithms. Here, we present the first frequency estimation algorithms for counting temporal motifs. More specifically, we develop a sampling framework that sits as a layer on top of existing exact counting algorithms and enables fast and accurate memory-efficient estimates of temporal motif counts. Our results show that we can achieve one to two orders of magnitude speedups with minimal and controllable loss in accuracy on a number of datasets.

Includes members:
  Member "a chronological edge driven approach to temporal subgraph isomorphism":
    also connected through other hyperedges:
      (none)
  Member "motifs in temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "enumerating maximal cliques in link streams with durations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enumerating isolated cliques in temporal networks")
  Member "when is a network a network multi order graphical model selection in pathways and temporal networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "honem network embedding using higher order patterns in sequential data")
  Member "science askscience and badscience on the coexistence of highly related communities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "retrieving top weighted triangles in graphs")
  Member "higher order organization of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "a fast and provable method for estimating clique counts using tur an s theorem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "scalable motif aware graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")

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)