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: "non linear distributed average consensus using bounded transmissions"
  Abstract: A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.

Connected through hyperedge HE-1 (induced by source paper "max consensus in sensor networks non linear bounded transmission and additive noise"):
  with members:
    - "robust analog function computation via wireless multiple access channels"
    - "finite time and asymptotic convergence of distributed averaging and maximizing algorithms"
    - "minimizing interference in ad hoc networks with bounded communication radius"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-2 (induced by source paper "robust consensus in the presence of impulsive channel noise"):
  with members:
    - "space time coding over fading channels with stable noise"

Connected through hyperedge HE-3 (induced by source paper "distributed estimation and detection with bounded transmissions over gaussian multiple access channels"):
  with members:
    - "robust analog function computation via wireless multiple access channels"
    - "distributed snr estimation using constant modulus signaling over gaussian multiple access channels"
    - "distributed detection over gaussian multiple access channels with constant modulus signaling"
    - "universal distributed estimation over multiple access channels with constant modulus signaling"
    - "estimation diversity and energy efficiency in distributed sensing"

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)