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 "parsimonious flooding in geometric random walks"):
  Abstract: We study the information spreading yielded by the \emph{(Parsimonious) $1$-Flooding Protocol} in geometric Mobile Ad-Hoc Networks. We consider $n$ agents on a convex plane region of diameter $D$ performing independent random walks with move radius $\rho$. At any time step, every active agent $v$ informs every non-informed agent which is within distance $R$ from $v$ ($R>0$ is the transmission radius). An agent is only active at the time step immediately after the one in which has been informed and, after that, she is removed. At the initial time step, a source agent is informed and we look at the \emph{completion time} of the protocol, i.e., the first time step (if any) in which all agents are informed. This random process is equivalent to the well-known \emph{Susceptible-Infective-Removed ($SIR$}) infection process in Mathematical Epidemiology. No analytical results are available for this random process over any explicit mobility model. The presence of removed agents makes this process much more complex than the (standard) flooding. We prove optimal bounds on the completion time depending on the parameters $n$, $D$, $R$, and $\rho$. The obtained bounds hold with high probability. We remark that our method of analysis provides a clear picture of the dynamic shape of the information spreading (or infection wave) over the time.

Includes members:
  Member "information spreading in stationary markovian evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how fast can we reach a target vertex in stochastic temporal graphs")
  Member "fast flooding over manhattan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved bounds on information dissemination by manhattan random waypoint model")
  Member "infectious random walks":
    also connected through other hyperedges:
      (none)
  Member "information propagation speed in mobile and delay tolerant networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links")
  Member "manets high mobility can make up for low transmission power":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "percolation of lipschitz surface and tight bounds on the spread of information among mobile agents")

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.SI(Social and Information Networks)