human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generating realistic scaled complex networks"):
  Abstract: Research on generative models is a central project in the emerging field of network science, and it studies how statistical patterns found in real networks could be generated by formal rules. Output from these generative models is then the basis for designing and evaluating computational methods on networks, and for verification and simulation studies. 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 ReCoN and some existing models can be fitted to an original network to produce a structurally similar replica, (c) use ReCoN to produce networks much larger than the original exemplar, and finally (d) discuss open problems and promising research directions. In a comparative experimental study, we find that ReCoN is often superior to many other state-of-the-art network generation methods. We argue that ReCoN is a scalable and effective tool for modeling a given network while preserving important properties at both micro- and macroscopic scales, and for scaling the exemplar data by orders of magnitude in size.

Includes members:
  Member "engineering parallel algorithms for community detection in massive networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high quality shared memory graph partitioning")
  Member "snap a general purpose network analysis and graph mining library":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive low level storage of very large knowledge graphs")
  Member "structure preserving sparsification methods for social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convex skeletons of complex networks")
  Member "generating scaled replicas of real world complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiscale planar graph generation")
  Member "drawing large graphs by multilevel maxent stress optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "networkit a tool suite for large scale complex network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")
  Member "a scalable generative graph model with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances in scalable network generation")
  Member "tree based coarsening and partitioning of complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithms for mapping parallel processes onto grid and torus architectures")

Question: Please tell me which class the source paper 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)