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 "group centrality maximization for large scale graphs"):
  Abstract: The study of vertex centrality measures is a key aspect of network analysis. Naturally, such centrality measures have been generalized to groups of vertices; for popular measures it was shown that the problem of finding the most central group is $\mathcal{NP}$-hard. As a result, approximation algorithms to maximize group centralities were introduced recently. Despite a nearly-linear running time, approximation algorithms for group betweenness and (to a lesser extent) group closeness are rather slow on large networks due to high constant overheads. #R##N#That is why we introduce GED-Walk centrality, a new submodular group centrality measure inspired by Katz centrality. In contrast to closeness and betweenness, it considers walks of any length rather than shortest paths, with shorter walks having a higher contribution. We define algorithms that (i) efficiently approximate the GED-Walk score of a given group and (ii) efficiently approximate the (proved to be $\mathcal{NP}$-hard) problem of finding a group with highest GED-Walk score. #R##N#Experiments on several real-world datasets show that scores obtained by GED-Walk improve performance on common graph mining tasks such as collective classification and graph-level classification. An evaluation of empirical running times demonstrates that maximizing GED-Walk (in approximation) is two orders of magnitude faster compared to group betweenness approximation and for group sizes $\leq 100$ one to two orders faster than group closeness approximation. For graphs with tens of millions of edges, approximate GED-Walk maximization typically needs less than one minute. Furthermore, our experiments suggest that the maximization algorithms scale linearly with the size of the input graph and the size of the group.

Includes members:
  Member "network density of states":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs")
  Member "current flow group closeness centrality for complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation")
  Member "pitfalls of graph neural network evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking graph neural networks")
  Member "scalable katz ranking computation in large static and dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "computing top k closeness centrality faster in unweighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "maximizing influence based group shapley centrality")
  Member "scaling up group closeness maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "maximizing influence based group shapley centrality")
  Member "generating massive complex networks with hyperbolic geometry faster in practice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances in scalable network generation")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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.DS(Data Structures and Algorithms)