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: "beeping a maximal independent set fast"
  Abstract: We adapt a recent algorithm by Ghaffari [SODA'16] for computing a Maximal Independent Set in the LOCAL model, so that it works in the significantly weaker BEEP model. For networks with maximum degree $\Delta$, our algorithm terminates locally within time $O((\log \Delta + \log (1/\epsilon)) \cdot \log(1/\epsilon))$, with probability at least $1 - \epsilon$. The key idea of the modification is to replace explicit messages about transmission probabilities with estimates based on the number of received messages. #R##N#After the successful introduction (and implicit use) of local analysis, e.g., by Barenboim et al. [JACM'16], Chung et al. [PODC'14], Ghaffari [SODA'16], and Halldorsson et al. [PODC'15], we study this concept in the BEEP model for the first time. #R##N#By doing so, we improve over local bounds that are implicitly derived from previous work (that uses traditional global analysis) on computing a Maximal Independent Set in the \beep model for a large range of values of the parameter $\Delta$. At the same time, we show that our algorithm in the \beep model only needs to pay a $\log(1/\epsilon)$ factor in the runtime compared to the best known MIS algorithm in the much more powerful \local model. We demonstrate that this overhead is negligible, as communication via beeps can be implemented using significantly less resources than communication in the LOCAL model. In particular, when looking at implementing these models, one round of the \local model needs at least $O(\Delta)$ time units, while one round in the BEEP model needs $O(\log\Delta)$ time units, an improvement that diminishes the loss of a $\log(1/\epsilon)$ factor in most settings.

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"
    - "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"
    - "the computational power of beeps"

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)