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: "identifying discriminating network motifs in youtube spam"
  Abstract: Like other social media websites, YouTube is not immune from the attention of spammers. In particular, evidence can be found of attempts to attract users to malicious thirdparty websites. As this type of spam is often associated with orchestrated campaigns, it has a discernible network signature, based on networks derived from comments posted by users to videos. In this paper, we examine examples of different YouTube spam campaigns of this nature, and use a feature selection process to identify network motifs that are characteristic of the corresponding campaign strategies. We demonstrate how these discriminating motifs can be used as part of a network motif proling process that tracks the activity of spam user accounts over time, enabling the process to scale to larger networks.

Connected through hyperedge HE-1 (induced by source paper "efficiently counting vertex orbits of all 5 vertex subgraphs by evoke"):
  with members:
    - "heterogeneous network motifs"
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-2 (induced by source paper "early detection of research trends"):
  with members:
    - "a bimodal network approach to model topic dynamics"
    - "maps of computer science"
    - "interactive overlays of journals and the measurement of interdisciplinarity on the basis of aggregated journal journal citations"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "the author topic model for authors and documents"
    - "development of computer science disciplines a social network analysis approach"
    - "interactive overlays a new method for generating global journal maps from web of science data"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-3 (induced by source paper "subgraph counting color coding beyond trees"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "why do simple algorithms for triangle enumeration work in the real world"
    - "network analysis of recurring youtube spam campaigns"

Connected through hyperedge HE-4 (induced by source paper "distributed estimation of graph 4 profiles"):
  with members:
    - "clique counting in mapreduce algorithms and experiments"
    - "approximately counting triangles in sublinear time"
    - "beyond triangles a distributed framework for estimating 3 profiles of large graphs"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "triadic measures on graphs the power of wedge sampling"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "a tail bound for read k families of functions"

Connected through hyperedge HE-5 (induced by source paper "beyond triangles a distributed framework for estimating 3 profiles of large graphs"):
  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"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "colorful triangle counting and a mapreduce implementation"

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)