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: "communicating with beeps"
  Abstract: The beep model is a very weak communications model in which devices in a network can communicate only via beeps and silence. As a result of its weak assumptions, it has broad applicability to many different implementations of communications networks. This comes at the cost of a restrictive environment for algorithm design. #R##N#Despite being only recently introduced, the beep model has received considerable attention, in part due to its relationship with other communication models such as that of ad-hoc radio networks. However, there has been no definitive published result for several fundamental tasks in the model. We aim to rectify this with our paper. #R##N#We present algorithms for the tasks of broadcast, gossiping, and multi-broadcast. Our $O(D+\log M)$-time algorithm for broadcasting is a simple formalization of a concept known as beep waves, and is asymptotically optimal. We give an $O(n \log L)$-time depth-first search procedure, and show how this can be used as the basis for an $O(n \log LM)$-time gossiping algorithm. Finally, we present almost optimal algorithms for the more general problem of multi-broadcast. When message provenance is required, we give an $O(k \log \frac{LM}{k}+D \log L)$-time algorithm and a corresponding $\Omega(k \log \frac{LM}{k}+D)$ lower bound. When provenance is not required, we give an algorithm taking $O(k \log \frac {M}{k}+D \log L)$ time when $M>k$ and $O(M+D \log L)$ otherwise, and a corresponding lower bound of $\Omega(k \log \frac{M}{k}+D)$ when $M>k$ and $\Omega(M+D)$ otherwise. #R##N#Our algorithms are all explicit, deterministic, and practical, and give efficient means of communication while making arguably the minimum possible assumptions about the network.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "noisy beeping networks"):
  with members:
    - "leader election in multi hop radio networks"
    - "efficient multiparty interactive coding for insertions deletions and substitutions"
    - "erasure correction for noisy radio networks"
    - "broadcasting in noisy radio 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 adversarial noise threshold for distributed protocols"

Connected through hyperedge HE-3 (induced by source paper "leader election in multi hop radio networks"):
  with members:
    - "near optimal leader election in multi hop radio networks"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-4 (induced by source paper "randomized communication without network knowledge"):
  with members:
    - "deterministic blind radio networks"
    - "deterministic communication in radio networks"
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "randomized broadcast in radio networks with collision detection"

Connected through hyperedge HE-5 (induced by source paper "naming a channel with beeps"):
  with members:
    - "randomized communication in radio networks"
    - "asynchronous broadcasting with bivalent beeps"
    - "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"
    - "randomized broadcast in radio networks with collision detection"
    - "beeping a maximal independent set"

Connected through hyperedge HE-6 (induced by source paper "asynchronous broadcasting with bivalent beeps"):
  with members:
    - "deterministic rendezvous with detection using beeps"
    - "the computational power of beeps"
    - "global synchronization and consensus using beeps in a fault prone mac"
    - "on distributed computing with beeps"
    - "beeping a maximal independent set"
    - "near optimal leader election in multi hop radio networks"
    - "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)