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: "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.

Connected through hyperedge HE-1 (induced by source paper "improved mixing time for k subgraph sampling"):
  with members:
    - "escape efficiently counting all 5 vertex subgraphs"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficiently estimating motif statistics of large networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-2 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"

Connected through hyperedge HE-3 (induced by source paper "ranking users in social networks with motif based pagerank"):
  with members:
    - "motifs in temporal networks"
    - "detecting strong ties using network motifs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "pagerank beyond the web"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "applying weighted pagerank to author citation networks"

Connected through hyperedge HE-4 (induced by source paper "motivo fast motif counting via succinct color coding and adaptive sampling"):
  with members:
    - "subgraph counting color coding beyond trees"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a general framework for estimating graphlet statistics via random walk"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficiently estimating motif statistics of large networks"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-5 (induced by source paper "multi motifgan mmgan motif targeted graph generation and prediction"):
  with members:
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "node2vec scalable feature learning for networks"
    - "scalable motif aware graph clustering"
    - "deepwalk online learning of social representations"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-6 (induced by source paper "graph pattern mining and learning through user defined relations extended version"):
  with members:
    - "higher order organization of complex networks"
    - "graph sample and hold a framework for big graph analytics"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-7 (induced by source paper "closed walk sampler an efficient method for estimating eigenvalues of large graphs"):
  with members:
    - "graph sample and hold a framework for big graph analytics"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "the internet as level topology three data sources and one definitive metric"

Connected through hyperedge HE-8 (induced by source paper "mining frequent patterns in evolving graphs"):
  with members:
    - "a general framework for estimating graphlet statistics via random walk"
    - "efficiently estimating motif statistics of large networks"

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)