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 "coloring large complex networks"):
  Abstract: Given a large social or information network, how can we partition the vertices into sets (i.e., colors) such that no two vertices linked by an edge are in the same set while minimizing the number of sets used. Despite the obvious practical importance of graph coloring, existing works have not systematically investigated or designed methods for large complex networks. In this work, we develop a unified framework for coloring large complex networks that consists of two main coloring variants that effectively balances the tradeoff between accuracy and efficiency. Using this framework as a fundamental basis, we propose coloring methods designed for the scale and structure of complex networks. In particular, the methods leverage triangles, triangle-cores, and other egonet properties and their combinations. We systematically compare the proposed methods across a wide range of networks (e.g., social, web, biological networks) and find a significant improvement over previous approaches in nearly all cases. Additionally, the solutions obtained are nearly optimal and sometimes provably optimal for certain classes of graphs (e.g., collaboration networks). We also propose a parallel algorithm for the problem of coloring neighborhood subgraphs and make several key observations. Overall, the coloring methods are shown to be (i) accurate with solutions close to optimal, (ii) fast and scalable for large networks, and (iii) flexible for use in a variety of applications.

Includes members:
  Member "hop doubling label indexing for point to point distance querying on scale free networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage")
  Member "graph sample and hold a framework for big graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph similarity learning a survey")
  Member "estimating clique composition and size distributions from sampled network data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating subgraph frequencies with or without attributes from egocentrically sampled data")
  Member "graph cluster randomization network exposure to multiple universes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inferring individual level causal models from graph based relational time series")
  Member "dimension reduction via colour refinement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards a practical k dimensional weisfeiler leman algorithm")
  Member "subgraph frequencies mapping the empirical and extremal geography of large graph collections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding the limitations of network online learning")
  Member "exact algorithms for maximum clique a computational study":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "replicable parallel branch and bound search")

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)