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: "incremental algorithms for network management and analysis based on closeness centrality"
  Abstract: Analyzing networks requires complex algorithms to extract meaningful information. Centrality metrics have shown to be correlated with the importance and loads of the nodes in network traffic. Here, we are interested in the problem of centrality-based network management. The problem has many applications such as verifying the robustness of the networks and controlling or improving the entity dissemination. It can be defined as finding a small set of topological network modifications which yield a desired closeness centrality configuration. As a fundamental building block to tackle that problem, we propose incremental algorithms which efficiently update the closeness centrality values upon changes in network topology, i.e., edge insertions and deletions. Our algorithms are proven to be efficient on many real-life networks, especially on small-world networks, which have a small diameter and a spike-shaped shortest distance distribution. In addition to closeness centrality, they can also be a great arsenal for the shortest-path-based management and analysis of the networks. We experimentally validate the efficiency of our algorithms on large networks and show that they update the closeness centrality values of the temporal DBLP-coauthorship network of 1.2 million users 460 times faster than it would take to compute them from scratch. To the best of our knowledge, this is the first work which can yield practical large-scale network management based on closeness centrality values.

Connected through hyperedge HE-1 (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-2 (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"
    - "computing classic closeness centrality at scale"
    - "social dynamics of digg"
    - "applying centrality measures to impact analysis a coauthorship network analysis"
    - "fast approximation of centrality"

Connected through hyperedge HE-3 (induced by source paper "disabling external influence in social networks via edge recommendation"):
  with members:
    - "modification of social dominance in social networks by selective adjustment of interpersonal weights"
    - "reducing controversy by connecting opposing views"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "updating and downdating techniques for optimizing network communicability"

Connected through hyperedge HE-4 (induced by source paper "estimating the degree centrality ranking of a node"):
  with members:
    - "betweenness centrality incremental and faster"
    - "graph size estimation"
    - "how to make the top ten approximating pagerank from in degree"
    - "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)