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: "distributed algorithms for computation of centrality measures in complex networks"
  Abstract: This paper is concerned with distributed computation of several commonly used centrality measures in complex networks. In particular, we propose deterministic algorithms, which converge in finite time, for the distributed computation of the degree, closeness and betweenness centrality measures in directed graphs. Regarding eigenvector centrality, we consider the PageRank problem as its typical variant, and design distributed randomized algorithms to compute PageRank for both fixed and time-varying graphs. A key feature of the proposed algorithms is that they do not require to know the network size, which can be simultaneously estimated at every node, and that they are clock-free. To address the PageRank problem of time-varying graphs, we introduce the concept of persistent graph, which eliminates the effect of spamming nodes. Moreover, we prove that these algorithms converge almost surely and in the sense of   $L^p$  . Finally, the effectiveness of the proposed algorithms is illustrated via extensive simulations using a classical benchmark.

Connected through hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"

Connected through hyperedge HE-2 (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"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "abra approximating betweenness centrality in static and dynamic graphs with rademacher averages"
    - "fully dynamic approximation of betweenness centrality"
    - "scalable online betweenness centrality in evolving graphs"
    - "approximating betweenness centrality in large evolving networks"

Connected through hyperedge HE-3 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling"):
  with members:
    - "tunable eigenvector based centralities for multiplex and temporal networks"
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "a new framework for centrality measures in multiplex networks"
    - "node and layer eigenvector centralities for multiplex networks"
    - "post processing partitions to identify domains of modularity optimization"
    - "a new framework for dynamical models on multiplex networks"
    - "block matrix formulations for evolving networks"
    - "pagerank beyond the web"

Connected through hyperedge HE-4 (induced by source paper "compressive closeness in networks"):
  with members:
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "defining and evaluating network communities based on ground truth"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "compressive sensing over graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "distributed algorithms for directed betweenness centrality and all pairs shortest paths"):
  with members:
    - "a deterministic distributed algorithm for exact weighted all pairs shortest paths in tilde o n 3 2 rounds"
    - "distributed exact weighted all pairs shortest paths in near linear time"
    - "distributed exact shortest paths in sublinear time"
    - "algebraic methods in the congested clique"
    - "distributed approximation algorithms for weighted shortest paths"

Connected through hyperedge HE-6 (induced by source paper "a tutorial on modeling and analysis of dynamic social networks part i"):
  with members:
    - "on the analysis of the degroot friedkin model with dynamic relative interaction matrices"
    - "opinion evolution in time varying social influence networks with prejudiced agents"
    - "differential inequalities in multi agent coordination and opinion dynamics modeling"
    - "novel multidimensional models of opinion dynamics in social networks"
    - "open multi agent systems gossiping with random arrivals and departures"
    - "active sensing of social networks"
    - "structural balance and opinion separation in trust mistrust social networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"

Connected through hyperedge HE-7 (induced by source paper "hellrank a hellinger based centrality measure for bipartite social networks"):
  with members:
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "user recommendations in reciprocal and bipartite social networks an online dating case study"
    - "identifying influential nodes in bipartite networks using the clustering coefficient"
    - "graph cluster randomization network exposure to multiple universes"
    - "network sampling from static to streaming graphs"
    - "benefits of bias towards better characterization of network 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.SY(Systems and Control)