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 fast sampling method of exploring graphlet degrees of large directed and undirected graphs"):
  Abstract: Exploring small connected and induced subgraph patterns (CIS patterns, or graphlets) has recently attracted considerable attention. Despite recent efforts on computing the number of instances a specific graphlet appears in a large graph (i.e., the total number of CISes isomorphic to the graphlet), little attention has been paid to characterizing a node's graphlet degree, i.e., the number of CISes isomorphic to the graphlet that include the node, which is an important metric for analyzing complex networks such as social and biological networks. Similar to global graphlet counting, it is challenging to compute node graphlet degrees for a large graph due to the combinatorial nature of the problem. Unfortunately, previous methods of computing global graphlet counts are not suited to solve this problem. In this paper we propose sampling methods to estimate node graphlet degrees for undirected and directed graphs, and analyze the error of our estimates. To the best of our knowledge, we are the first to study this problem and give a fast scalable solution. We conduct experiments on a variety of real-word datasets that demonstrate that our methods accurately and efficiently estimate node graphlet degrees for graphs with millions of edges.

Includes members:
  Member "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "number of connected components in a graph estimation via counting patterns")
  Member "distributed estimation of graph 4 profiles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a fast counting method for 6 motifs with low connectivity")
  Member "minfer inferring motif statistics from sampled edges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a general framework for estimating graphlet statistics via random walk")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the power of pivoting for exact clique counting")
  Member "graph sample and hold a framework for big graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph similarity learning a survey")
  Member "efficiently estimating motif statistics of large networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved mixing time for k subgraph sampling")
  Member "a space efficient streaming algorithm for triangle counting using the birthday paradox":
    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)