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: "counting in one hop beeping networks"
  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. Beeping models are weak in essence and even simple tasks may become difficult or unfeasible with them. #R##N#In this paper, we address the problem of computing how many participants there are in a one-hop network: the {\em counting} problem. We first observe that no algorithm can compute this number with certainty in $BL$, whether the algorithm be deterministic or even randomised (Las Vegas). We thus consider the stronger variant where beeping nodes are able to detect simultaneous beeps, referred to as $B_{cd}L$ (for {\em collision detection}). We prove that at least $n$ rounds are necessary in $B_{cd}L$, and we present an algorithm whose running time is $O(n)$ rounds with high probability. Further experimental results show that its expected running time is less than $10n$. Finally, we discuss how this algorithm can be adapted in other beeping models. In particular, we show that it can be emulated in $BL$, at the cost of a logarithmic slowdown and of trading its Las Vegas nature (result certain, time uncertain) against Monte Carlo (time certain, result uncertain).

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "design patterns in beeping algorithms examples emulation and analysis"):
  with members:
    - "randomized communication in radio networks"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "the computational power of beeps"
    - "randomized broadcast in radio networks with collision detection"
    - "the locality of distributed symmetry breaking"
    - "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)