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 general framework for estimating graphlet statistics via random walk"):
  Abstract: Graphlets are induced subgraph patterns and have been frequently applied to characterize the local topology structures of graphs across various domains, e.g., online social networks (OSNs) and biological networks. Discovering and computing graphlet statistics are highly challenging. First, the massive size of real-world graphs makes the exact computation of graphlets extremely expensive. Secondly, the graph topology may not be readily available so one has to resort to web crawling using the available application programming interfaces (APIs). In this work, we propose a general and novel framework to estimate graphlet statistics of "any size". Our framework is based on collecting samples through consecutive steps of random walks. We derive an analytical bound on the sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator. To further improve the accuracy, we introduce two novel optimization techniques to reduce the lower bound on the sample size. Experimental evaluations demonstrate that our methods outperform the state-of-the-art method up to an order of magnitude both in terms of accuracy and time cost.

Includes members:
  Member "estimating subgraph frequencies with or without attributes from egocentrically sampled data":
    also connected through other hyperedges:
      (none)
  Member "leveraging history for faster sampling of online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sampling online social networks by random walk with indirect jumps")
  Member "beyond triangles a distributed framework for estimating 3 profiles of large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "waddling random walk fast and accurate mining of motif statistics in large graphs")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "deep graph similarity learning a survey")
  Member "graph based anomaly detection and description a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")

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)