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: "shortest fastest and foremost broadcast in dynamic networks"
  Abstract: Highly dynamic networks rarely offer end-to-end connectivity at a given time. Yet, connectivity in these networks can be established over time and space, based on temporal analogues of multi-hop paths (also called {\em journeys}). Attempting to optimize the selection of the journeys in these networks naturally leads to the study of three cases: shortest (minimum hop), fastest (minimum duration), and foremost (earliest arrival) journeys. Efficient centralized algorithms exists to compute all cases, when the full knowledge of the network evolution is given. #R##N#In this paper, we study the {\em distributed} counterparts of these problems, i.e. shortest, fastest, and foremost broadcast with termination detection (TDB), with minimal knowledge on the topology. #R##N#We show that the feasibility of each of these problems requires distinct features on the evolution, through identifying three classes of dynamic graphs wherein the problems become gradually feasible: graphs in which the re-appearance of edges is {\em recurrent} (class R), {\em bounded-recurrent} (B), or {\em periodic} (P), together with specific knowledge that are respectively $n$ (the number of nodes), $\Delta$ (a bound on the recurrence time), and $p$ (the period). In these classes it is not required that all pairs of nodes get in contact -- only that the overall {\em footprint} of the graph is connected over time. #R##N#Our results, together with the strict inclusion between $P$, $B$, and $R$, implies a feasibility order among the three variants of the problem, i.e. TDB[foremost] requires weaker assumptions on the topology dynamics than TDB[shortest], which itself requires less than TDB[fastest]. Reversely, these differences in feasibility imply that the computational powers of $R_n$, $B_\Delta$, and $P_p$ also form a strict hierarchy.

Connected through hyperedge HE-1 (induced by source paper "the computational complexity of finding temporal paths under waiting time constraints"):
  with members:
    - "an adaptive version of brandes algorithm for betweenness centrality"
    - "efficient computation of optimal temporal walks under waiting time constraints"
    - "how fast can we reach a target vertex in stochastic temporal graphs"
    - "temporal cliques admit sparse spanners"
    - "fixed parameter algorithms for maximum profit facility location under matroid constraints"
    - "the temporal explorer who returns to the base"
    - "changing times to minimise reachability in temporal graphs"
    - "deleting edges to restrict the size of an epidemic in temporal networks"

Connected through hyperedge HE-2 (induced by source paper "on computing distances and latencies in link streams"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-3 (induced by source paper "local distributed algorithms in highly dynamic networks"):
  with members:
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic maximal independent set with sublinear update time"
    - "improved algorithms for fully dynamic maximal independent set"
    - "the cost of global broadcast in dynamic radio networks"
    - "information spreading in dynamic networks under oblivious adversaries"
    - "optimal dynamic distributed mis"
    - "enabling minimal dominating set in highly dynamic distributed systems"
    - "an improved distributed algorithm for maximal independent set"

Connected through hyperedge HE-4 (induced by source paper "gracefully degrading gathering in dynamic rings"):
  with members:
    - "stream graphs and link streams for the modeling of interactions over time"
    - "gathering in dynamic rings"
    - "computability of perpetual exploration in highly dynamic rings"
    - "self stabilizing robots in highly dynamic environments"
    - "gracefully degrading consensus and k set agreement in directed dynamic networks"
    - "live exploration of dynamic rings"
    - "the next 700 impossibility results in time varying graphs"
    - "introducing speculation in self stabilization an application to mutual exclusion"

Connected through hyperedge HE-5 (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-6 (induced by source paper "live exploration of dynamic rings"):
  with members:
    - "an introduction to temporal graphs an algorithmic perspective"
    - "on temporal graph exploration"
    - "naming and counting in anonymous unknown dynamic networks"
    - "lower bounds on information dissemination in dynamic networks"
    - "agreement in directed dynamic networks"
    - "information spreading in dynamic graphs"
    - "information spreading in stationary markovian evolving graphs"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-7 (induced by source paper "efficiently testing t interval connectivity in dynamic graphs"):
  with members:
    - "self stabilizing robots in highly dynamic environments"
    - "the next 700 impossibility results in time varying graphs"
    - "dmvp foremost waypoint coverage of time varying graphs"
    - "testing temporal connectivity in sparse dynamic graphs"
    - "temporal reachability graphs"
    - "characterizing topological assumptions of distributed algorithms in dynamic networks"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-8 (induced by source paper "enabling minimal dominating set in highly dynamic distributed systems"):
  with members:
    - "the next 700 impossibility results in time varying graphs"
    - "temporal reachability graphs"
    - "characterizing topological assumptions of distributed algorithms in dynamic networks"
    - "on the feasibility of maintenance algorithms in dynamic graphs"
    - "time varying graphs and 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.DC(Distributed, Parallel, and Cluster Computing)