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: "directed closure measures for networks with reciprocity"
  Abstract: The study of triangles in graphs is a standard tool in network analysis, leading to measures such as the \emph{transitivity}, i.e., the fraction of paths of length $2$ that participate in triangles. Real-world networks are often directed, and it can be difficult to "measure" this network structure meaningfully. We propose a collection of \emph{directed closure values} for measuring triangles in directed graphs in a way that is analogous to transitivity in an undirected graph. Our study of these values reveals much information about directed triadic closure. For instance, we immediately see that reciprocal edges have a high propensity to participate in triangles. We also observe striking similarities between the triadic closure patterns of different web and social networks. We perform mathematical and empirical analysis showing that directed configuration models that preserve reciprocity cannot capture the triadic closure patterns of real networks.

Connected through hyperedge HE-1 (induced by source paper "measuring directed triadic closure with closure coefficients"):
  with members:
    - "the switch markov chain for sampling irregular graphs"
    - "friendship networks and social status"
    - "community structure and scale free collections of erdős renyi graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "on large scale graph generation with validation of diverse triangle statistics at edges and vertices"):
  with members:
    - "design generation and validation of extreme scale power law graphs"
    - "distributed triangle counting in the graphulo matrix math library"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "detecting highly cyclic structure with complex eigenpairs"):
  with members:
    - "tensor spectral clustering for partitioning higher order network structures"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "using triangles to improve community detection in directed networks"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
    - "clustering and community detection in directed networks a survey"
    - "triadic measures on graphs the power of wedge sampling"
    - "scalable spectral algorithms for community detection in directed networks"

Connected through hyperedge HE-4 (induced by source paper "using triangles to improve community detection in directed networks"):
  with members:
    - "clustering and community detection in directed networks a survey"
    - "shaping communities out of triangles"
    - "community structure and scale free collections of erdős renyi graphs"
    - "mutual or unrequited love identifying stable clusters in social networks with uni and bi directional links"

Connected through hyperedge HE-5 (induced by source paper "wedge sampling for computing clustering coefficients and triangle counts on large graphs"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "triadic measures on graphs the power of wedge sampling"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "degree relations of triangles in real world networks and graph models"
    - "community structure and scale free collections of erdős renyi graphs"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-6 (induced by source paper "reciprocal versus parasocial relationships in online social networks"):
  with members:
    - "dynamics of trust reciprocation in heterogenous mmog networks"
    - "multi scale dynamics in a massive online social network"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"
    - "nonparametric link prediction in dynamic networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "predicting links and inferring attributes using a social attribute network san"
    - "four degrees of separation"

Connected through hyperedge HE-7 (induced by source paper "a space efficient streaming algorithm for triangle counting using the birthday paradox"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "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)