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: "centrality metric for dynamic networks"
  Abstract: Centrality is an important notion in network analysis and is used to measure the degree to which network structure contributes to the importance of a node in a network. While many different centrality measures exist, most of them apply to static networks. Most networks, on the other hand, are dynamic in nature, evolving over time through the addition or deletion of nodes and edges. A popular approach to analyzing such networks represents them by a static network that aggregates all edges observed over some time period. This approach, however, under or overestimates centrality of some nodes. We address this problem by introducing a novel centrality metric for dynamic network analysis. This metric exploits an intuition that in order for one node in a dynamic network to influence another over some period of time, there must exist a path that connects the source and destination nodes through intermediaries at different times. We demonstrate on an example network that the proposed metric leads to a very different ranking than analysis of an equivalent static network. We use dynamic centrality to study a dynamic citations network and contrast results to those reached by static network analysis.

Connected through hyperedge HE-1 (induced by source paper "nonlinearity networks a 2020 vision"):
  with members:
    - "models of continuous time networks with tie decay diffusion and convection"
    - "tunable eigenvector based centralities for multiplex and temporal networks"
    - "architecture and evolution of semantic networks in mathematics texts"
    - "supracentrality analysis of temporal networks with directed interlayer coupling"
    - "using persistent homology to quantify a diurnal cycle in hurricane felix"
    - "persistent homology of geospatial data a case study with voting"
    - "network structure from rich but noisy data"
    - "random walks on simplicial complexes and the normalized hodge 1 laplacian"

Connected through hyperedge HE-2 (induced by source paper "using core periphery structure to predict high centrality nodes in time varying networks"):
  with members:
    - "time series analysis of temporal networks"
    - "understanding stability of noisy networks through centrality measures and local connections"
    - "centrality metrics and localization in core periphery networks"
    - "predicting node degree centrality with the node prominence profile"
    - "locally estimating core numbers"

Connected through hyperedge HE-3 (induced by source paper "non altering time scales for aggregation of dynamic networks into series of graphs"):
  with members:
    - "handling oversampling in dynamic networks using link prediction"
    - "detecting change points in the large scale structure of evolving networks"
    - "identifying roles in an ip network with temporal and structural density"
    - "influence of the dynamic social network timeframe type and size on the group evolution discovery"

Connected through hyperedge HE-4 (induced by source paper "hiding individuals and communities in a social network"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "de anonymizing social networks"

Connected through hyperedge HE-5 (induced by source paper "centrality measures for graphons accounting for uncertainty in networks"):
  with members:
    - "graphons a nonparametric method to model estimate and design algorithms for massive networks"
    - "distributions of centrality on networks"
    - "stability and continuity of centrality measures in weighted graphs"
    - "pagerank beyond the web"
    - "co authorship networks in the digital library research community"

Connected through hyperedge HE-6 (induced by source paper "the many faces of graph dynamics"):
  with members:
    - "time centrality in dynamic complex networks"
    - "modeling and measuring graph similarity the case for centrality distance"
    - "deltacon a principled massive graph similarity function"
    - "multi scale dynamics in a massive online social network"

Connected through hyperedge HE-7 (induced by source paper "exploring the evolution of node neighborhoods in dynamic networks"):
  with members:
    - "interpreting communities based on the evolution of a dynamic attributed network"
    - "a method for characterizing communities in dynamic attributed complex networks"
    - "role dynamics fast mining of large dynamic networks"

Connected through hyperedge HE-8 (induced by source paper "maximizing coverage centrality via network design extended version"):
  with members:
    - "towards scalable network delay minimization"
    - "scalable betweenness centrality maximization via sampling"

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.CY(Computers and Society)