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: "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
  Abstract: We present ABRA, a suite of algorithms that compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms rely on random sampling and their analysis leverages on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, this is the first application of these concepts to the field of graph analysis. The results of our experimental evaluation show that our approach is much faster than exact methods, and vastly outperforms, in both speed and number of samples, current state-of-the-art algorithms with the same quality guarantees.

Connected through hyperedge HE-1 (induced by source paper "simple and fast distributed computation of betweenness centrality"):
  with members:
    - "distributed estimation and control of node centrality in undirected asymmetric networks"
    - "distributed algorithms for directed betweenness centrality and all pairs shortest paths"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "fully dynamic approximation of betweenness centrality"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"

Connected through hyperedge HE-2 (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"
    - "scaling up group closeness maximization"
    - "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-3 (induced by source paper "shortest path centrality and the all pairs shortest paths problem via vc dimension theory"):
  with members:
    - "estimating the percolation centrality of large networks through pseudo dimension theory"
    - "almost shortest paths and pram distance oracles in weighted graphs"
    - "approximating apsp without scaling equivalence of approximate min plus and exact min max"
    - "on the shoshan zwick algorithm for the all pairs shortest path problem"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "faster all pairs shortest paths via circuit complexity"
    - "shape fitting on point sets with probability distributions"

Connected through hyperedge HE-4 (induced by source paper "estimating the percolation centrality of large networks through pseudo dimension theory"):
  with members:
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-5 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-6 (induced by source paper "learning to identify high betweenness centrality nodes from scratch a novel graph neural network approach"):
  with members:
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "enhanced network embeddings via exploiting edge labels"
    - "graph edit distance computation via graph neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "representation learning on graphs methods and applications"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-7 (induced by source paper "guidelines for experimental algorithmics in network analysis"):
  with members:
    - "a gpu based solution to fast calculation of betweenness centrality on large weighted networks"
    - "shared memory parallel subgraph enumeration"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "route planning in transportation networks"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"
    - "axioms for centrality"

Connected through hyperedge HE-8 (induced by source paper "learning equilibria of simulation based games"):
  with members:
    - "a generalised method for empirical game theoretic analysis"
    - "aivat a new variance reduction technique for agent evaluation in imperfect information games"
    - "learning equilibria of games via payoff queries"
    - "analyzing incentives for protocol compliance in complex domains a case study of introduction based routing"
    - "query complexity of approximate nash equilibria"

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)