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: "scaling up group closeness maximization"
  Abstract: Closeness is a widely-used centrality measure in social network analysis. For a node it indicates the inverse average shortest-path distance to the other nodes of the network. While the identification of the k nodes with highest closeness received significant attention, many applications are actually interested in finding a group of nodes that is central as a whole. For this problem, only recently a greedy algorithm with approximation ratio (1-1/e) has been proposed [Chen et al., ADC 2016]. Since this algorithm's running time is still expensive for large networks, a heuristic without approximation guarantee has also been proposed in the same paper. In the present paper we develop new techniques to speed up the greedy algorithm without losing its theoretical guarantee. Compared to a straightforward implementation, our approach is orders of magnitude faster and, compared to the heuristic proposed by Chen et al., we always find a solution with better quality in a comparable running time in our experiments. Our method Greedy++ allows us to approximate the group with maximum closeness on networks with up to hundreds of millions of edges in minutes or at most a few hours. To have the same theoretical guarantee, the greedy approach by [Chen et al., ADC 2016] would take several days already on networks with hundreds of thousands of edges. In a comparison with the optimum, our experiments show that the solution found by Greedy++ is actually much better than the theoretical guarantee. Over all tested networks, the empirical approximation ratio is never lower than 0.97. Finally, we study for the first time the correlation between the top-k nodes with highest individual closeness and an approximation of the most central group in large networks. Our results show that the overlap between the two is relatively small, which indicates empirically the need to distinguish between the two problems.

Connected through hyperedge HE-1 (induced by source paper "maximizing influence based group shapley centrality"):
  with members:
    - "game theoretic network centrality a review"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "axioms for centrality"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-2 (induced by source paper "current flow group closeness centrality for complex networks"):
  with members:
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "scalable betweenness centrality maximization via sampling"
    - "measuring and maximizing influence via random walk in social activity networks"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "absorbing random walk centrality theory and algorithms"
    - "axioms for centrality"
    - "approximate maximum flow on separable undirected graphs"

Connected through hyperedge HE-3 (induced by source paper "group centrality maximization for large scale graphs"):
  with members:
    - "network density of states"
    - "current flow group closeness centrality for complex networks"
    - "pitfalls of graph neural network evaluation"
    - "scalable katz ranking computation in large static and dynamic graphs"
    - "computing top k closeness centrality faster in unweighted graphs"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "semi supervised classification with graph convolutional networks"
    - "scalable betweenness centrality maximization via sampling"

Connected through hyperedge HE-4 (induced by source paper "local search for group closeness maximization on big graphs"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "powers of tensors and fast matrix multiplication"
    - "networkit a tool suite for large scale complex network analysis"
    - "axioms for centrality"

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)