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: "optimal leader election in multi hop radio networks"
  Abstract: We present two optimal randomized leader election algorithms for multi-hop radio networks, which run in expected time asymptotically equal to the time required to broadcast one message to the entire network. We first observe that, under certain assumptions, a simulation approach of Bar-Yehuda, Golreich and Itai (1991) can be used to obtain an algorithm that for directed and undirected networks elects a leader in O(D log n D + log 2 n) expected time, where n is the number of the nodes and D is the eccentricity or the diameter of the network. We then extend this approach and present a second algorithm, which operates on undirected multi-hop radio networks with collision detection and elects a leader in O(D + logn) expected run-time. This algorithm in fact operates on the beep model, a strictly weaker model in which nodes can only communicate via beeps or silence. Both of these algorithms are optimal; no optimal expected-time algorithms for these models have been previously known. We further apply our techniques to design an algorithm that is quicker to achieve leader election with high probability. We give an algorithm for the model without collision detection which always runs in time O((D log n D +log 2 n)· √ logn), and succeeds with high probability. While non-optimal, and indeed slightly slower than the algorithm of Ghaffari and Haeupler (2013), it has the advantage of working in directed networks; it is the fastest known leader election a lgorithm to achieve a high-probability bound in such circumstances.

Connected through hyperedge HE-1 (induced by source paper "exponential separations in the energy complexity of leader election"):
  with members:
    - "the energy complexity of broadcast"
    - "broadcasting in noisy radio networks"
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "the computational power of beeps"
    - "radio network lower bounds made easy"
    - "near optimal leader election in multi hop radio networks"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-2 (induced by source paper "how to elect a low energy leader"):
  with members:
    - "the computational power of beeps"
    - "radio network lower bounds made easy"
    - "near optimal leader election in multi hop radio 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.DC(Distributed, Parallel, and Cluster Computing)