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: "fast parallel graphlet counting for large networks"
  Abstract: From social science to biology, numerous applications often rely on motifs for intuitive and meaningful characterization of networks at both the global macro-level as well as the local micro-level. While motifs have witnessed a tremendous success and impact in a variety of domains, there has yet to be a fast and efficient approach for computing the frequencies of these subgraph patterns. However, existing methods are not scalable to large networks with millions of nodes and edges, which impedes the application of motifs to new problems that require large-scale network analysis. To address these problems, we propose a fast, efficient, and parallel algorithm for counting motifs of size $k=\{3,4\}$-nodes that take only a fraction of the time to compute when compared with the current methods used. The proposed motif counting algorithms leverages a number of proven combinatorial arguments for different motifs. For each edge, we count a few motifs, and with these counts along with the combinatorial arguments, we obtain the exact counts of others in constant time. On a large collection of $300$+ networks from a variety of domains, our motif counting strategies are on average 460x faster than current methods. This brings new opportunities to investigate the use of motifs on much larger networks and newer applications as we show in our experiments. To the best of our knowledge, this paper provides the largest motif computations to date as well as the largest systematic investigation on over 300+ networks from a variety of domains.

Connected through hyperedge HE-1 (induced by source paper "the resistance perturbation distance a metric for the analysis of dynamic networks"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "graphlet decomposition framework algorithms and applications"
    - "anomaly detection in dynamic networks of varying size"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"
    - "practical graph isomorphism ii"
    - "spectral sparsification of graphs"

Connected through hyperedge HE-2 (induced by source paper "escape efficiently counting all 5 vertex subgraphs"):
  with members:
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "distributed estimation of graph 4 profiles"
    - "counting triangles in massive graphs with mapreduce"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

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)