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: "fast and simple computation of top k closeness centralities"
  Abstract: Closeness is an important centrality measure widely used in the analysis of real-world complex networks. In particular, the problem of selecting the k most central nodes with respect to this measure has been deeply analyzed in the last decade. However, even for not very large networks, this problem is computationally intractable in practice: indeed, Abboud et al have recently shown that its complexity is strictly related to the complexity of the All-Pairs Shortest Path (in short, APSP) problem, for which no subcubic "combinatorial" algorithm is known. In this paper, we propose a new algorithm for selecting the k most closeness central nodes in a graph. In practice, this algorithm significantly improves over the APSP approach, even though its worst-case time complexity is the same. For example, the algorithm is able to compute the top k nodes in few dozens of seconds even when applied to real-world networks with millions of nodes and edges. We will also experimentally prove that our algorithm drastically outperforms the most recently designed algorithm, proposed by Olsen et al. Finally, we apply the new algorithm to the computation of the most central actors in the IMDB collaboration network, where two actors are linked if they played together in a movie.

Connected through hyperedge HE-1 (induced by source paper "computing top k closeness centrality faster in unweighted graphs"):
  with members:
    - "a note on the complexity of computing the number of reachable vertices in a digraph"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "powers of tensors and fast matrix multiplication"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "into the square on the complexity of quadratic time solvable problems"

Connected through hyperedge HE-2 (induced by source paper "scaling up group closeness maximization"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "engineering parallel algorithms for community detection in massive networks"
    - "scalable betweenness centrality maximization via sampling"
    - "average distance queries through weighted samples in graphs and metric spaces high scalability with tight statistical guarantees"
    - "networkit a tool suite for large scale complex network analysis"
    - "computing classic closeness centrality at scale"
    - "fast approximation of centrality"

Connected through hyperedge HE-3 (induced by source paper "one class classifiers based on entropic spanning graphs"):
  with members:
    - "toward a multilevel representation of protein molecules"
    - "entropic one class classifiers"
    - "modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one class classification"
    - "a review of feature selection methods based on mutual information"

Connected through hyperedge HE-4 (induced by source paper "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "snap a general purpose network analysis and graph mining library"
    - "algorithmic complexity of power law networks"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "networkit an interactive tool suite for high performance network analysis"
    - "into the square on the complexity of quadratic time solvable problems"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-5 (induced by source paper "approximating betweenness centrality in fully dynamic networks"):
  with members:
    - "fully dynamic approximation of betweenness centrality"
    - "a faster algorithm for fully dynamic betweenness centrality"
    - "networkit an interactive tool suite for high performance network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"
    - "betweenness centrality incremental and faster"

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.DS(Data Structures and Algorithms)