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: "a unifying model for representing time varying graphs"
  Abstract: Graph-based models form a fundamental aspect of data representation in Data Sciences and play a key role in modeling complex networked systems. In particular, recently there is an ever-increasing interest in modeling dynamic complex networks, i.e. networks in which the topological structure (nodes and edges) may vary over time. In this context, we propose a novel model for representing finite discrete Time-Varying Graphs (TVGs), which are typically used to model dynamic complex networked systems. We analyze the data structures built from our proposed model and demonstrate that, for most practical cases, the asymptotic memory complexity of our model is in the order of the cardinality of the set of edges. Further, we show that our proposal is an unifying model that can represent several previous (classes of) models for dynamic networks found in the recent literature, which in general are unable to represent each other. In contrast to previous models, our proposal is also able to intrinsically model cyclic (i.e. periodic) behavior in dynamic networks. These representation capabilities attest the expressive power of our proposed unifying model for TVGs. We thus believe our unifying model for TVGs is a step forward in the theoretical foundations for data analysis of complex networked systems.

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 "degree based outlier detection within ip traffic modelled as a link stream"):
  with members:
    - "non altering time scales for aggregation of dynamic networks into series of graphs"
    - "stream graphs and link streams for the modeling of interactions over time"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "an algebraic approach to temporal network analysis based on temporal quantities"
    - "network traffic anomaly detection"
    - "graph based anomaly detection and description a survey"
    - "temporal reachability graphs"
    - "netsimile a scalable approach to size independent network similarity"

Connected through hyperedge HE-3 (induced by source paper "weighted bipartite or directed stream graphs for the modeling of temporal networks"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "on multiaspect graphs"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-4 (induced by source paper "fair end to end window based congestion control in time varying data communication networks"):
  with members:
    - "time varying network modeling and its optimal routing strategy"
    - "multiaspect graphs algebraic representation and algorithms"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "a tutorial on clique problems in communications and signal processing"):
  with members:
    - "eptas for max clique on disks and unit balls"
    - "enhanced uplink resource allocation in non orthogonal multiple access systems"
    - "discovering patterns of interest in ip traffic using cliques in bipartite link streams"
    - "delivery time reduction for order constrained applications using binary network codes"
    - "data dissemination using instantly decodable binary codes in fog radio access networks"
    - "on multiaspect graphs"
    - "distributed hybrid scheduling in multi cloud networks using conflict graphs"
    - "resilient backhaul network design using hybrid radio free space optical technology"

Connected through hyperedge HE-6 (induced by source paper "the temporal event graph"):
  with members:
    - "an introduction to temporal graphs an algorithmic perspective"

Connected through hyperedge HE-7 (induced by source paper "time varying network modeling and its optimal routing strategy"):
  with members:
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-8 (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"

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)