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: "generating scaled replicas of real world complex networks"
  Abstract: Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks can be generated by formal rules. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how models can be fitted to an original network to produce a structurally similar replica, and (c) aim for producing much larger networks than the original exemplar. In a comparative experimental study, we find ReCoN often superior to many other state-of-the-art network generation methods. Our design yields a scalable and effective tool for replicating a given network while preserving important properties at both micro- and macroscopic scales and (optionally) scaling the replica by orders of magnitude in size. We recommend ReCoN as a general practical method for creating realistic test data for the engineering of computational methods on networks, verification, and simulation studies. We provide scalable open-source implementations of most studied methods, including ReCoN.

Connected through hyperedge HE-1 (induced by source paper "multiscale planar graph generation"):
  with members:
    - "single and multi level network sparsification by algebraic distance"
    - "generating realistic scaled complex networks"
    - "networkit an interactive tool suite for high performance network analysis"
    - "a multilevel bilinear programming algorithm for the vertex separator problem"
    - "recent advances in graph partitioning"
    - "community structure and scale free collections of erdős renyi graphs"
    - "advanced coarsening schemes for graph partitioning"
    - "multiscale network generation"

Connected through hyperedge HE-2 (induced by source paper "updating dynamic random hyperbolic graphs in sublinear time"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "geometric inhomogeneous random graphs"
    - "approximating betweenness centrality in fully dynamic networks"
    - "on the diameter of hyperbolic random graphs"
    - "fast generation of complex networks with underlying hyperbolic geometry"

Connected through hyperedge HE-3 (induced by source paper "the impact of partially missing communities on the reliability of centrality measures"):
  with members:
    - "estimating the sensitivity of centrality measures w r t measurement errors"
    - "networkit a tool suite for large scale complex network analysis"

Connected through hyperedge HE-4 (induced by source paper "a generative model for sparse evolving digraphs"):
  with members:
    - "a large scale study of call graph based impact prediction using mutation testing"
    - "a generative model of software dependency graphs to better understand software evolution"

Connected through hyperedge HE-5 (induced by source paper "generating realistic scaled complex networks"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "drawing large graphs by multilevel maxent stress optimization"
    - "networkit a tool suite for large scale complex network analysis"
    - "a scalable generative graph model with community structure"
    - "tree based coarsening and partitioning of complex networks"
    - "recent advances in graph partitioning"

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)