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: "design patterns in beeping algorithms"
  Abstract: We consider networks of processes which interact with beeps. In the basic model defined by Cornejo and Kuhn, which we refer to as the $BL$ variant, processes can choose in each round either to beep or to listen. Those who beep are unable to detect simultaneous beeps. Those who listen can only distinguish between silence and the presence of at least one beep. Stronger variants exist where the nodes can also detect collision while they are beeping ($B_{cd}L$) or listening ($BL_{cd}$), or both ($B_{cd}L_{cd}$). #R##N#This paper starts with a discussion on generic building blocks ({\em design patterns}) which seem to occur frequently in the design of beeping algorithms. They include {\em multi-slot phases}, {\em exclusive beeps}, {\em adaptive probability}, {\em internal} (resp. {\em peripheral}) collision detection, and {\em emulation} of collision detection when it is not available as a primitive. #R##N#The paper then provides algorithms for a number of basic problems, including colouring, 2-hop colouring, degree computation, 2-hop MIS, and collision detection (in $BL$). Using the patterns, we formulate these algorithms in a rather concise and intuitive way. Their analyses are more technical. One of them relies on a Martingale technique with non-independent variables. Another of our analyses improves that of the MIS algorithm by Jeavons et al. by getting rid of a gigantic constant (the asymptotic order was already optimal). #R##N#Finally, we study the relative power of several variants of beeping models. In particular, we explain how {\em every} Las Vegas algorithm with collision detection can be converted, through emulation, into a Monte Carlo algorithm without, at the cost of a logarithmic slowdown. We prove this is optimal up to a constant factor by giving a matching lower bound, and provide an example of use for solving the MIS problem in $BL$.

Connected through hyperedge HE-1 (induced by source paper "counting in one hop beeping networks"):
  with members:
    - "randomized communication in radio networks"
    - "scalable wake up of multi channel single hop radio networks"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "the computational power of beeps"
    - "naming and counting in anonymous unknown dynamic networks"
    - "beeping a maximal independent set"
    - "feedback from nature an optimal distributed algorithm for maximal independent set selection"
    - "deploying wireless networks with 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)