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 "waddling random walk fast and accurate mining of motif statistics in large graphs"):
  Abstract: Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration is infeasible for either its prohibitive computational costs or access restrictions on the full graph data. Methods to estimate the motif statistics based on random walks by sampling only a small fraction of the subgraphs in the large graph address both of these challenges. In this paper, we present a new algorithm, called the Waddling Random Walk (WRW), which estimates the concentration of motifs of any size. It derives its name from the fact that it sways a little to the left and to the right, thus also sampling nodes not directly on the path of the random walk. The WRW algorithm achieves its computational efficiency by not trying to enumerate subgraphs around the random walk but instead using a randomized protocol to sample subgraphs in the neighborhood of the nodes visited by the walk. In addition, WRW achieves significantly higher accuracy (measured by the closeness of its estimate to the correct value) and higher precision (measured by the low variance in its estimations) than the current state-of-the-art algorithms for mining subgraph statistics. We illustrate these advantages in speed, accuracy and precision using simulations on well-known and widely used graph datasets representing real networks.

Includes members:
  Member "beyond triangles a distributed framework for estimating 3 profiles of large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a fast counting method for 6 motifs with low connectivity")
  Member "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "number of connected components in a graph estimation via counting patterns")
  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 "subgraph frequencies mapping the empirical and extremal geography of large graph collections":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "estimating and sampling graphs with multidimensional random walks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms")

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)