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: "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 $D$.

Connected through hyperedge HE-1 (induced by source paper "distributed voting in beep model"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "beeping a maximal independent set fast"
    - "deterministic rendezvous with detection using beeps"
    - "counting in one hop beeping networks"
    - "naming a channel with beeps"
    - "asynchronous broadcasting with bivalent beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"

Connected through hyperedge HE-2 (induced by source paper "noisy beeping networks"):
  with members:
    - "leader election in multi hop radio networks"
    - "efficient multiparty interactive coding for insertions deletions and substitutions"
    - "erasure correction for noisy radio networks"
    - "broadcasting in noisy radio networks"
    - "naming a channel with beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "communicating with beeps"

Connected through hyperedge HE-3 (induced by source paper "leader election in multi hop radio networks"):
  with members:
    - "communicating with beeps"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-4 (induced by source paper "deterministic communication in radio networks"):
  with members:
    - "randomized broadcast in radio networks with collision detection"
    - "information dissemination in unknown radio networks with large labels"

Connected through hyperedge HE-5 (induced by source paper "the energy complexity of broadcast"):
  with members:
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "exponential separations in the energy complexity of leader election"
    - "randomized broadcast in radio networks with collision detection"
    - "radio network lower bounds made easy"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-6 (induced by source paper "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"):
  with members:
    - "randomized broadcast in radio networks with collision detection"
    - "parallel graph decompositions using random shifts"

Connected through hyperedge HE-7 (induced by source paper "an optimal randomized broadcasting algorithm in radio networks with collision detection"):
  with members:
    - "randomized broadcast in radio networks with collision detection"

Connected through hyperedge HE-8 (induced by source paper "naming a channel with beeps"):
  with members:
    - "randomized communication in radio networks"
    - "asynchronous broadcasting with bivalent beeps"
    - "scalable wake up of multi channel single hop radio networks"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "the computational power of beeps"
    - "anonymous processors with synchronous shared memory"
    - "communicating with beeps"
    - "randomized broadcast in radio networks with collision detection"

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.DS(Data Structures and Algorithms)