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: "asynchronous broadcasting with bivalent beeps"
  Abstract: In broadcasting, one node of a network has a message that must be learned by all other nodes. We study deterministic algorithms for this fundamental communication task in a very weak model of wireless communication. The only signals sent by nodes are beeps. Moreover, they are delivered to neighbors of the beeping node in an asynchronous way: the time between sending and reception is finite but unpredictable. We first observe that under this scenario, no communication is possible, if beeps are all of the same strength. Hence we study broadcasting in the bivalent beeping model, where every beep can be either soft or loud. At the receiving end, if exactly one soft beep is received by a node in a round, it is heard as soft. Any other combination of beeps received in a round is heard as a loud beep. The cost of a broadcasting algorithm is the total number of beeps sent by all nodes. #R##N#We consider four levels of knowledge that nodes may have about the network: anonymity (no knowledge whatsoever), ad-hoc (all nodes have distinct labels and every node knows only its own label), neighborhood awareness (every node knows its label and labels of all neighbors), and full knowledge (every node knows the entire labeled map of the network and the identity of the source). We first show that in the anonymous case, broadcasting is impossible even for very simple networks. For each of the other three knowledge levels we provide upper and lower bounds on the minimum cost of a broadcasting algorithm. Our results show separations between all these scenarios. Perhaps surprisingly, the jump in broadcasting cost between the ad-hoc and neighborhood awareness levels is much larger than between the neighborhood awareness and full knowledge levels, although in the two former levels knowledge of nodes is local, and in the latter it is global.

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"
    - "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 "naming a channel with beeps"):
  with members:
    - "randomized communication in radio networks"
    - "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"
    - "beeping a maximal independent set"

Connected through hyperedge HE-3 (induced by source paper "communicating with beeps"):
  with members:
    - "leader election in multi hop radio networks"
    - "naming a channel with beeps"
    - "the computational power of beeps"
    - "global synchronization and consensus using beeps in a fault prone mac"
    - "deterministic broadcasting and gossiping with beeps"
    - "randomized broadcast in radio networks with collision detection"
    - "beeping a maximal independent set"
    - "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)