human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "lower bounds on information dissemination in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the communication cost of information spreading in dynamic networks")
  Member "agreement in directed dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "consensus in rooted dynamic networks with short lived stability")
  Member "characterizing topological assumptions of distributed algorithms in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed online data aggregation in dynamic graphs")
  Member "information spreading in stationary markovian evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how fast can we reach a target vertex in stochastic temporal graphs")
  Member "time varying graphs and dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "edge disjoint branchings in temporal graphs")
  Member "information propagation speed in mobile and delay tolerant networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links")
  Member "exploration of periodically varying graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal vertex cover with a sliding time window")
  Member "the computational power of population protocols":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust comparison in population protocols")

Question: Please tell me which class the source paper 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)