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: "manets high mobility can make up for low transmission power"
  Abstract: We consider a Mobile Ad-hoc NETworks (MANET) formed by "n" nodes that move independently at random over a finite square region of the plane. Nodes exchange data if they are at distance at most "r" within each other, where r>0 is the node transmission radius. The "flooding time" is the number of time steps required to broadcast a message from a source node to every node of the network. Flooding time is an important measure of the speed of information spreading in dynamic networks. #R##N#We derive a nearly-tight upper bound on the flooding time which is a decreasing function of the maximal "velocity" of the nodes. It turns out that, when the node velocity is sufficiently high, even if the node transmission radius "r" is far below the "connectivity threshold", the flooding time does not asymptotically depend on "r". This implies that flooding can be very fast even though every "snapshot" (i.e. the static random geometric graph at any fixed time) of the MANET is fully disconnected. Data reach all nodes quickly despite these ones use very low transmission power. #R##N#Our result is the first analytical evidence of the fact that high, random node mobility strongly speed-up information spreading and, at the same time, let nodes save energy.

Connected through hyperedge HE-1 (induced by source paper "percolation of lipschitz surface and tight bounds on the spread of information among mobile agents"):
  with members:
    - "information spreading in stationary markovian evolving graphs"

Connected through hyperedge HE-2 (induced by source paper "mobile conductance in sparse networks and mobility connectivity tradeoff"):
  with members:
    - "mobile conductance and gossip based information spreading in mobile networks"
    - "gossip based information spreading in mobile networks"
    - "tight bounds on information dissemination in sparse mobile networks"
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-3 (induced by source paper "gossip based information spreading in mobile networks"):
  with members:
    - "tight bounds on information dissemination in sparse mobile networks"
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "the impact of mobility on gossip algorithms"
    - "product multicommodity flow in wireless networks"
    - "throughput and delay in random wireless networks with restricted mobility"
    - "fast distributed algorithms for computing separable functions"

Connected through hyperedge HE-4 (induced by source paper "information spreading in dynamic graphs"):
  with members:
    - "tight bounds on information dissemination in sparse mobile networks"
    - "information spreading in stationary markovian evolving graphs"
    - "flooding time in opportunistic networks under power law and exponential inter contact times"
    - "performance of opportunistic epidemic routing on edge markovian dynamic graphs"
    - "fast flooding over manhattan"
    - "modelling mobility a discrete revolution"
    - "information propagation speed in mobile and delay tolerant networks"

Connected through hyperedge HE-5 (induced by source paper "flooding time in opportunistic networks under power law and exponential inter contact times"):
  with members:
    - "tight bounds on information dissemination in sparse mobile networks"
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "swim a simple model to generate small mobile worlds"

Connected through hyperedge HE-6 (induced by source paper "parsimonious flooding in geometric random walks"):
  with members:
    - "information spreading in stationary markovian evolving graphs"
    - "fast flooding over manhattan"
    - "infectious random walks"
    - "information propagation speed in mobile and delay tolerant networks"

Connected through hyperedge HE-7 (induced by source paper "tight bounds on information dissemination in sparse mobile networks"):
  with members:
    - "information spreading in stationary markovian evolving graphs"

Connected through hyperedge HE-8 (induced by source paper "information spreading in stationary markovian evolving graphs"):
  with members:
    - "information propagation speed in mobile and delay tolerant 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.DM(Discrete Mathematics)