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 top k closeness centrality faster in unweighted graphs"
  Abstract: Given a connected graph $G=(V,E)$, the closeness centrality of a vertex $v$ is defined as $\frac{n-1}{\sum_{w \in V} d(v,w)}$. This measure is widely used in the analysis of real-world complex networks, and the problem of selecting the $k$ most central vertices has been deeply analysed in the last decade. However, this problem is computationally not easy, especially for large networks: in the first part of the paper, we prove that it is not solvable in time $\O(|E|^{2-\epsilon})$ on directed graphs, for any constant $\epsilon>0$, under reasonable complexity assumptions. Furthermore, we propose a new algorithm for selecting the $k$ most central nodes in a graph: we experimentally show that this algorithm improves significantly both the textbook algorithm, which is based on computing the distance between all pairs of vertices, and the state of the art. For example, we are able to compute the top $k$ nodes in few dozens of seconds in real-world networks with millions of nodes and edges. Finally, as a case study, we compute the $10$ most central actors in the IMDB collaboration network, where two actors are linked if they played together in a movie, and in the Wikipedia citation network, which contains a directed edge from a page $p$ to a page $q$ if $p$ contains a link to $q$.

Connected through hyperedge HE-1 (induced by source paper "maximizing influence based group shapley centrality"):
  with members:
    - "game theoretic network centrality a review"
    - "scaling up group closeness maximization"
    - "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 "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"
    - "scaling up group closeness maximization"
    - "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-3 (induced by source paper "local search for group closeness maximization on big graphs"):
  with members:
    - "scaling up group closeness maximization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "global rank estimation"):
  with members:
    - "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"
    - "fully dynamic approximation of betweenness centrality"

Connected through hyperedge HE-5 (induced by source paper "improving the betweenness centrality of a node by adding links"):
  with members:
    - "faster betweenness centrality updates in evolving networks"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "scalable betweenness centrality maximization via sampling"
    - "approximating betweenness centrality in fully dynamic networks"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "networkit a tool suite for large scale complex network analysis"
    - "scalable online betweenness centrality in evolving graphs"

Connected through hyperedge HE-6 (induced by source paper "a faster method to estimate closeness centrality ranking"):
  with members:
    - "degree ranking using local information"
    - "rank me thou shalln t compare me"
    - "estimating the degree centrality ranking of a node"
    - "computing classic closeness centrality at scale"
    - "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-7 (induced by source paper "scaling up group closeness maximization"):
  with members:
    - "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"
    - "computing classic closeness centrality at scale"
    - "fast approximation of centrality"

Connected through hyperedge HE-8 (induced by source paper "discriminative distance based network indices with application to link prediction"):
  with members:
    - "motifs in temporal networks"
    - "link prediction using top k shortest distances"
    - "generating online social networks based on socio demographic attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "fast approximation of 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)