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: "core periphery structure in networks"
  Abstract: Intermediate-scale (or `meso-scale') structures in networks have received considerable attention, as the algorithmic detection of such structures makes it possible to discover network features that are not apparent either at the local scale of nodes and edges or at the global scale of summary statistics. Numerous types of meso-scale structures can occur in networks, but investigations of such features have focused predominantly on the identification and study of community structure. In this paper, we develop a new method to investigate the meso-scale feature known as core-periphery structure, which entails identifying densely-connected core nodes and sparsely-connected periphery nodes. In contrast to communities, the nodes in a core are also reasonably well-connected to those in the periphery. Our new method of computing core-periphery structure can identify multiple cores in a network and takes different possible cores into account. We illustrate the differences between our method and several existing methods for identifying which nodes belong to a core, and we use our technique to examine core-periphery structure in examples of friendship, collaboration, transportation, and voting networks.

Connected through hyperedge HE-1 (induced by source paper "spectral density of equitable core periphery graphs"):
  with members:
    - "spectral partitioning in equitable graphs"
    - "centrality metrics and localization in core periphery networks"
    - "identification of core periphery structure in networks"
    - "localization and centrality in networks"

Connected through hyperedge HE-2 (induced by source paper "uncovering complex overlapping pattern of communities in large scale social networks"):
  with members:
    - "community detection algorithm evaluation with ground truth data"
    - "the ground truth about metadata and community detection in networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "efficient detection of communities with significant overlaps in networks partial community merger algorithm"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "graph spectra and the detectability of community structure in networks"
    - "a large scale community structure analysis in facebook"

Connected through hyperedge HE-3 (induced by source paper "phase transition of the 2 choices dynamics on core periphery networks"):
  with members:
    - "tight analysis for the 3 majority consensus dynamics"
    - "the coalescing branching random walk on expanders and the dual epidemic process"
    - "bounds on the voter model in dynamic networks"
    - "fast plurality consensus in regular expanders"
    - "discordant voting processes on finite graphs"
    - "stabilizing consensus with many opinions"
    - "core periphery in networks an axiomatic approach"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-4 (induced by source paper "convexity in complex networks"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "higher order organization of complex networks"
    - "detection of core periphery structure in networks using spectral methods and geodesic paths"
    - "representing higher order dependencies in networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identification of core periphery structure in networks"
    - "network lasso clustering and optimization in large graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "random spatial network models with core periphery structure"):
  with members:
    - "higher order clustering in networks"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "found graph data and planted vertex covers"
    - "coin flipping ball dropping and grass hopping for generating random graphs from matrices of edge probabilities"
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "detection of core periphery structure in networks using spectral methods and geodesic paths"
    - "identification of core periphery structure in networks"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-6 (induced by source paper "on rich clubs of path based centralities in networks"):
  with members:
    - "locally estimating core numbers"
    - "truss decomposition in massive networks"
    - "multiscale network generation"
    - "the rich club phenomenon in the internet topology"
    - "accurately modeling the internet topology"

Connected through hyperedge HE-7 (induced by source paper "detection of core periphery structure in networks using spectral methods and geodesic paths"):
  with members:
    - "centrality metrics and localization in core periphery networks"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "identification of core periphery structure in networks"
    - "role discovery in networks"
    - "a local perspective on community structure in multilayer networks"
    - "social structure of facebook networks"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "structure and overlaps of communities in networks"

Connected through hyperedge HE-8 (induced by source paper "neighbor neighbor correlations explain measurement bias in networks"):
  with members:
    - "the majority illusion in social networks"
    - "identification of core periphery structure in networks"
    - "network weirdness exploring the origins of network paradoxes"
    - "generalized friendship paradox in complex networks the case of scientific collaboration"
    - "friendship paradox redux your friends are more interesting than you"
    - "virality prediction and community structure in social networks"
    - "systematic topology analysis and generation using degree correlations"

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)