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: "time centrality in dynamic complex networks"
  Abstract: There is an ever-increasing interest in investigating dynamics in time-varying graphs (TVGs). Nevertheless, so far, the notion of centrality in TVG scenarios usually refers to metrics that assess the relative importance of nodes along the temporal evolution of the dynamic complex network. For some TVG scenarios, however, more important than identifying the central nodes under a given node centrality definition is identifying the key time instants for taking certain actions. In this paper, we thus introduce and investigate the notion of time centrality in TVGs. Analogously to node centrality, time centrality evaluates the relative importance of time instants in dynamic complex networks. In this context, we present two time centrality metrics related to diffusion processes. We evaluate the two defined metrics using both a real-world dataset representing an in-person contact dynamic network and a synthetically generated randomized TVG. We validate the concept of time centrality showing that diffusion starting at the best ranked time instants (i.e., the most central ones), according to our metrics, can perform a faster and more efficient diffusion process.

Connected through hyperedge HE-1 (induced by source paper "algorithmic networks central time to trigger expected emergent open endedness"):
  with members:
    - "self referential basis of undecidable dynamics from the liar paradox and the halting problem to the edge of chaos"
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "quantifying synergistic information using intermediate stochastic variables"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "computing halting probabilities from other halting probabilities"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "relativizing an incompressible number and an incompressible function through subrecursive extensions of turing machines"
    - "on multiaspect graphs"

Connected through hyperedge HE-2 (induced by source paper "learning the undecidable from networked systems"):
  with members:
    - "algorithmically probable mutations reproduce aspects of evolution such as convergence rate genetic memory and modularity"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "undecidability and irreducibility conditions for open ended evolution and emergence"
    - "quantifying synergistic mutual information"

Connected through hyperedge HE-3 (induced by source paper "introducing multilayer stream graphs and layer centralities"):
  with members:
    - "multilayer network model of movie script"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "centrality measures for networks with community structure"

Connected through hyperedge HE-4 (induced by source paper "algorithmically random generalized graphs and their topological properties"):
  with members:
    - "low algorithmic complexity entropy deceiving graphs"
    - "the paradox of computability and a recursive relative version of the busy beaver function"
    - "correlation of automorphism group size and topological properties with program size complexity evaluations of graphs and complex networks"
    - "calculating kolmogorov complexity from the output frequency distributions of small turing machines"

Connected through hyperedge HE-5 (induced by source paper "expected emergence of algorithmic information from a lower bound for stationary prevalence"):
  with members:
    - "algorithmic networks central time to trigger expected emergent open endedness"

Connected through hyperedge HE-6 (induced by source paper "on incompressible multidimensional networks"):
  with members:
    - "emergent open endedness from contagion of the fittest"

Connected through hyperedge HE-7 (induced by source paper "stream graphs and link streams for the modeling of interactions over time"):
  with members:
    - "degree based outlier detection within ip traffic modelled as a link stream"
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "movie rating prediction using content based and link stream features"
    - "motifs in temporal networks"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "time series analysis of temporal networks"
    - "graphprints towards a graph analytic method for network anomaly detection"
    - "semirings for temporal network analysis"

Connected through hyperedge HE-8 (induced by source paper "the many faces of graph dynamics"):
  with members:
    - "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"
    - "centrality metric for dynamic networks"

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.SI(Social and Information Networks)