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: "minfer inferring motif statistics from sampled edges"
  Abstract: Characterizing motif (i.e., locally connected subgraph patterns) statistics is important for understanding complex networks such as online social networks and communication networks. Previous work made the strong assumption that the graph topology of interest is known, and that the dataset either fits into main memory or stored on disks such that it is not expensive to obtain all neighbors of any given node. In practice, researchers have to deal with the situation where the graph topology is unknown, either because the graph is dynamic, or because it is expensive to collect and store all topological and meta information on disk. Hence, what is available to researchers is only a snapshot of the graph generated by sampling edges from the graph at random, which we called a "RESampled graph". Clearly, a RESampled graph's motif statistics may be quite different from the underlying original graph. To solve this challenge, we propose a framework and implement a system called Minfer, which can take the given RESampled graph and accurately infer the underlying graph's motif statistics. We also use Fisher information to bound the error of our estimates. Experiments using large scale datasets show that our method to be accurate.

Connected through hyperedge HE-1 (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"
    - "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets"
    - "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-2 (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"
    - "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets"
    - "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-3 (induced by source paper "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"):
  with members:
    - "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets"
    - "distributed estimation of graph 4 profiles"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "moss a scalable tool for efficiently sampling and counting 4 and 5 node graphlets"):
  with members:
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "distributed graphlab a framework for machine learning in the cloud"
    - "defining and evaluating network communities based on ground truth"
    - "the slashdot zoo mining a social network with negative edges"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)