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 "near optimal leader election in multi hop radio networks"):
  Abstract: We present distributed randomized leader election protocols for multi-hop radio networks that elect a leader in almost the same time $T_{BC}$ required for broadcasting a message. For the setting without collision detection, our algorithm runs with high probability in $O(D \log \frac{n}{D} + \log^3 n) \min\{\log\log n,\log \frac{n}{D}\}$ rounds on any $n$-node network with diameter $D$. Since $T_{BC} = \Theta(D \log \frac{n}{D} + \log^2 n)$ is a lower bound, our upper bound is optimal up to a factor of at most $\log \log n$ and the extra $\log n$ factor on the additive term. This algorithm is furthermore the first $O(n)$ time algorithm for this setting. #R##N#Our algorithms improve over a 25 year old simulation approach of Bar-Yehuda, Goldreich and Itai with a $O(T_{BC} \log n)$ running time: In 1987 they designed a fast broadcast protocol and subsequently in 1989 they showed how it can be used to simulate one round of a single-hop network that has collision detection in $T_{BC}$ time. The prime application of this simulation was to simulate Willards single-hop leader election protocol, which elects a leader in $O(\log n)$ rounds with high probability and $O(\log \log n)$ rounds in expectation. While it was subsequently shown that Willards bounds are tight, it was unclear whether the simulation approach is optimal. Our results break this barrier and essentially remove the logarithmic slowdown over the broadcast time $T_{BC}$ by going away from the simulation approach. #R##N#We also give a distributed randomized leader election algorithm for the setting with collision detection that runs in $O(D + \log n \log \log n) \cdot \min\{\log \log n, \log \frac{n}{D}\}$ rounds. This round complexity is optimal up to $O(\log \log n)$ factors and improves over a deterministic algorithm that requires $\Theta(n)$ rounds independently of the diameter $D$.

Includes members:
  Member "randomized broadcast in radio networks with collision detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")
  Member "broadcast throughput in radio networks routing vs network coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "synchronous transmissions in low power wireless a survey of communication protocols and network services")
  Member "distributed deterministic broadcasting in uniform power ad hoc wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deterministic protocols in the sinr model without knowledge of coordinates")
  Member "fast structuring of radio networks large for multi message communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "broadcasting in noisy radio networks")
  Member "bounded contention coding for wireless networks in the high snr regime":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross sender bit mixing coding")
  Member "beeping a maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")
  Member "wireless connectivity and capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "analyzing network coding gossip made easy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "synchronous transmissions in low power wireless a survey of communication protocols and network services")

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.DS(Data Structures and Algorithms)