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: "tight bounds on information dissemination in sparse mobile networks"
  Abstract: Motivated by the growing interest in mobile systems, we study the dynamics of information dissemination between agents moving independently on a plane. Formally, we consider $k$ mobile agents performing independent random walks on an $n$-node grid. At time $0$, each agent is located at a random node of the grid and one agent has a rumor. The spread of the rumor is governed by a dynamic communication graph process ${G_t(r) | t \geq 0}$, where two agents are connected by an edge in $G_t(r)$ iff their distance at time $t$ is within their transmission radius $r$. Modeling the physical reality that the speed of radio transmission is much faster than the motion of the agents, we assume that the rumor can travel throughout a connected component of $G_t$ before the graph is altered by the motion. We study the broadcast time $T_B$ of the system, which is the time it takes for all agents to know the rumor. We focus on the sparse case (below the percolation point $r_c \approx \sqrt{n/k}$) where, with high probability, no connected component in $G_t$ has more than a logarithmic number of agents and the broadcast time is dominated by the time it takes for many independent random walks to meet each other. Quite surprisingly, we show that for a system below the percolation point the broadcast time does not depend on the relation between the mobility speed and the transmission radius. In fact, we prove that $T_B = \tilde{O}(n / \sqrt{k})$ for any $0 \leq r < r_c$, even when the transmission range is significantly larger than the mobility range in one step, giving a tight characterization up to logarithmic factors. Our result complements a recent result of Peres et al. (SODA 2011) who showed that above the percolation point the broadcast time is polylogarithmic in $k$.

Connected through hyperedge HE-1 (induced by source paper "improved bounds on information dissemination by manhattan random waypoint model"):
  with members:
    - "information spreading in dynamic graphs"
    - "inferring networks of diffusion and influence"
    - "fast flooding over manhattan"
    - "modelling mobility a discrete revolution"

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"
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "manets high mobility can make up for low transmission power"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-3 (induced by source paper "on the role of mobility for multimessage gossip"):
  with members:
    - "information spreading in dynamic graphs"
    - "information spreading in stationary markovian evolving graphs"
    - "analyzing network coding gossip made easy"
    - "distributed averaging via lifted markov chains"
    - "the impact of mobility on gossip algorithms"
    - "gossiping with multiple messages"
    - "fast distributed algorithms for computing separable functions"

Connected through hyperedge HE-4 (induced by source paper "gossip based information spreading in mobile networks"):
  with members:
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "manets high mobility can make up for low transmission power"
    - "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-5 (induced by source paper "enhancing information dissemination in dynamic wireless network using stability and beamforming"):
  with members:
    - "improving link prediction in intermittently connected wireless networks by considering link and proximity stabilities"

Connected through hyperedge HE-6 (induced by source paper "information spreading in dynamic graphs"):
  with members:
    - "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"
    - "manets high mobility can make up for low transmission power"

Connected through hyperedge HE-7 (induced by source paper "flooding time in opportunistic networks under power law and exponential inter contact times"):
  with members:
    - "information spreading in stationary markovian evolving graphs"
    - "information propagation speed in mobile and delay tolerant networks"
    - "manets high mobility can make up for low transmission power"
    - "swim a simple model to generate small mobile worlds"

Connected through hyperedge HE-8 (induced by source paper "fast flooding over manhattan"):
  with members:
    - "information spreading in dynamic graphs"
    - "information spreading in stationary markovian evolving graphs"
    - "modelling mobility a discrete revolution"
    - "information propagation speed in mobile and delay tolerant networks"
    - "manets high mobility can make up for low transmission power"
    - "swim a simple model to generate small mobile worlds"

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)