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 "guidelines for experimental algorithmics in network analysis"):
  Abstract: The field of network science is a highly interdisciplinary area; for the empirical analysis of network data, it draws algorithmic methodologies from several research fields. Hence, research procedures and descriptions of the technical results often differ, sometimes widely. In this paper we focus on methodologies for the experimental part of algorithm engineering for network analysis -- an important ingredient for a research area with empirical focus. More precisely, we unify and adapt existing recommendations from different fields and propose universal guidelines -- including statistical analyses -- for the systematic evaluation of network analysis algorithms. This way, the behavior of newly proposed algorithms can be properly assessed and comparisons to existing solutions become meaningful. Moreover, as the main technical contribution, we provide SimexPal, a highly automated tool to perform and analyze experiments following our guidelines. To illustrate the merits of SimexPal and our guidelines, we apply them in a case study: we design, perform, visualize and evaluate experiments of a recent algorithm for approximating betweenness centrality, an important problem in network analysis. In summary, both our guidelines and SimexPal shall modernize and complement previous efforts in experimental algorithmics; they are not only useful for network analysis, but also in related contexts.

Includes members:
  Member "a gpu based solution to fast calculation of betweenness centrality on large weighted networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "betweenness centrality is more parallelizable than dense matrix multiplication")
  Member "shared memory parallel subgraph enumeration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient implementation of color coding algorithm for subgraph isomorphism problem")
  Member "kadabra is an adaptive algorithm for betweenness via random approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")
  Member "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simple and fast distributed computation of betweenness centrality")
  Member "route planning in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  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 "scalable online betweenness centrality in evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "simple and fast distributed computation of betweenness centrality")
  Member "approximating betweenness centrality in large evolving networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "simple and fast distributed computation of betweenness centrality")

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)