human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets"
  Abstract: Counting the frequencies of 3-, 4-, and 5-node undirected motifs (also know as graphlets) is widely used for understanding complex networks such as social and biology networks. However, it is a great challenge to compute these metrics for a large graph due to the intensive computation. Despite recent efforts to count triangles (i.e., 3-node undirected motif counting), little attention has been given to developing scalable tools that can be used to characterize 4- and 5-node motifs. In this paper, we develop computational efficient methods to sample and count 4- and 5- node undirected motifs. Our methods provide unbiased estimators of motif frequencies, and we derive simple and exact formulas for the variances of the estimators. Moreover, our methods are designed to fit vertex centric programming models, so they can be easily applied to current graph computing systems such as Pregel and GraphLab. We conduct experiments on a variety of real-word datasets, and experimental results show that our methods are several orders of magnitude faster than the state-of-the-art methods under the same estimation errors.

Connected through hyperedge HE-1 (induced by source paper "number of connected components in a graph estimation via counting patterns"):
  with members:
    - "homomorphisms are a good basis for counting small subgraphs"
    - "distributed estimation of generalized matrix rank efficient algorithms and lower bounds"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triadic measures on graphs the power of wedge sampling"

Connected through hyperedge HE-2 (induced by source paper "a general framework for estimating graphlet statistics via random walk"):
  with members:
    - "estimating subgraph frequencies with or without attributes from egocentrically sampled data"
    - "leveraging history for faster sampling of online social networks"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "minfer inferring motif statistics from sampled edges"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "graph based anomaly detection and description a survey"
    - "efficiently estimating motif statistics of large networks"

Connected through hyperedge HE-3 (induced by source paper "waddling random walk fast and accurate mining of motif statistics in large graphs"):
  with members:
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "minfer inferring motif statistics from sampled edges"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "efficiently estimating motif statistics of large networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-4 (induced by source paper "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"):
  with members:
    - "distributed estimation of graph 4 profiles"
    - "minfer inferring motif statistics from sampled edges"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "efficiently estimating motif statistics of large networks"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"

Question: Please tell me which class the center node 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)