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: "computing classic closeness centrality at scale"
  Abstract: Closeness centrality, first considered by Bavelas (1948), is an importance measure of a node in a network which is based on the distances from the node to all other nodes. The classic definition, proposed by Bavelas (1950), Beauchamp (1965), and Sabidussi (1966), is (the inverse of) the average distance to all other nodes.   We propose the first highly scalable (near linear-time processing and linear space overhead) algorithm for estimating, within a small relative error, the classic closeness centralities of all nodes in the graph. Our algorithm applies to undirected graphs, as well as for centrality computed with respect to round-trip distances in directed graphs.   For directed graphs, we also propose an efficient algorithm that approximates generalizations of classic closeness centrality to outbound and inbound centralities. Although it does not provide worst-case theoretical approximation guarantees, it is designed to perform well on real networks.   We perform extensive experiments on large networks, demonstrating high scalability and accuracy.

Connected through hyperedge HE-1 (induced by source paper "machine learning in network centrality measures tutorial and outlook"):
  with members:
    - "an analysis of centrality measures for complex and social networks"
    - "a scalable generative graph model with community structure"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "representation learning a review and new perspectives"
    - "community structure and scale free collections of erdős renyi graphs"
    - "defining and evaluating network communities based on ground truth"
    - "applying centrality measures to impact analysis a coauthorship network analysis"

Connected through hyperedge HE-2 (induced by source paper "global rank estimation"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "literature survey on interplay of topics information diffusion and connections on social networks"
    - "group centrality for semantic networks a swot analysis featuring random walks"
    - "centrality measures in networks based on nodes attributes long range interactions and group influence"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"

Connected through hyperedge HE-3 (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"
    - "fast and simple computation of top k closeness centralities"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "optimizing node discovery on networks problem definitions fast algorithms and observations"):
  with members:
    - "optimal tagging with markov chain optimization"
    - "powerwalk scalable personalized pagerank via random walks with vertex centric decomposition"
    - "scalable betweenness centrality maximization via sampling"
    - "absorbing random walk centrality theory and algorithms"
    - "axioms for centrality"
    - "random walk domination in large graphs problem definitions and fast solutions"
    - "using friends as sensors to detect global scale contagious outbreaks"
    - "a tractable approach to finding closest truncated commute time neighbors in large graphs"

Connected through hyperedge HE-5 (induced by source paper "a faster method to estimate closeness centrality ranking"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "degree ranking using local information"
    - "rank me thou shalln t compare me"
    - "estimating the degree centrality ranking of a node"
    - "incremental algorithms for network management and analysis based on closeness centrality"
    - "social dynamics of digg"
    - "applying centrality measures to impact analysis a coauthorship network analysis"
    - "fast approximation of centrality"

Connected through hyperedge HE-6 (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"
    - "fast and simple computation of top k closeness centralities"
    - "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"
    - "fast approximation of centrality"

Connected through hyperedge HE-7 (induced by source paper "an analysis of centrality measures for complex and social networks"):
  with members:
    - "heuristic for network coverage optimization applied in finding organizational change agents"

Connected through hyperedge HE-8 (induced by source paper "approximation and fixed parameter subquadratic algorithms for radius and diameter"):
  with members:
    - "on problems as hard as cnf sat"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "quadratic time hardness of lcs and other sequence similarity measures"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "into the square on the complexity of quadratic time solvable problems"
    - "computing the diameter polynomially faster than apsp"
    - "subgraph isomorphism in planar graphs and related problems"

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)