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: "network based consensus averaging with general noisy channels"
  Abstract: This paper focuses on the consensus averaging problem on graphs under general noisy channels. We study a particular class of distributed consensus algorithms based on damped updates, and using the ordinary differential equation method, we prove that the updates converge almost surely to exact consensus for finite variance noise. Our analysis applies to various types of stochastic disturbances, including errors in parameters, transmission noise, and quantization noise. Under a suitable stability condition, we prove that the error is asymptotically Gaussian, and we show how the asymptotic covariance is specified by the graph Laplacian. For additive parameter noise, we show how the scaling of the asymptotic MSE is controlled by the spectral gap of the Laplacian.

Connected through hyperedge HE-1 (induced by source paper "asymptotic properties of distributed social sampling algorithm"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "consensus conditions of continuous time multi agent systems with time delays and measurement noises"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"

Connected through hyperedge HE-2 (induced by source paper "scale free loopy structure is resistant to noise in consensus dynamics in complex networks"):
  with members:
    - "maximum matchings and minimum dominating sets in apollonian networks and extended tower of hanoi graphs"
    - "domination number and minimum dominating sets in pseudofractal scale free web and sierpinski graph"
    - "control principles of complex networks"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "consensus and coherence in fractal networks"

Connected through hyperedge HE-3 (induced by source paper "source coding optimization for distributed average consensus"):
  with members:
    - "performance limits for noisy multimeasurement vector problems"
    - "an overview of multi processor approximate message passing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "optimal trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "distributed parameter estimation with quantized communication via running average"
    - "on constrained randomized quantization"
    - "toward resource optimal consensus over the wireless medium"

Connected through hyperedge HE-4 (induced by source paper "generalized geometric programming for rate allocation in consensus"):
  with members:
    - "an overview of multi processor approximate message passing"
    - "optimal trade offs in multi processor approximate message passing"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "performance trade offs in multi processor approximate message passing"
    - "toward resource optimal consensus over the wireless medium"
    - "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"
    - "gossip algorithms for distributed signal processing"
    - "distributed lossy averaging"

Connected through hyperedge HE-5 (induced by source paper "distributed averaging with random network graphs and noises"):
  with members:
    - "multi agent consensus with relative state dependent measurement noises"
    - "sensor networks with random links topology design for distributed consensus"

Connected through hyperedge HE-6 (induced by source paper "approximate consensus multi agent control under stochastic environment with application to load balancing"):
  with members:
    - "smoothed analysis of balancing networks"

Connected through hyperedge HE-7 (induced by source paper "convergence speed of the consensus algorithm with interference and sparse long range connectivity"):
  with members:
    - "location aided fast distributed consensus in wireless networks"
    - "sensor networks with random links topology design for distributed consensus"
    - "distance distributions in finite uniformly random networks theory and applications"
    - "geographic gossip efficient aggregation for sensor networks"

Connected through hyperedge HE-8 (induced by source paper "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"):
  with members:
    - "distributed lossy averaging"
    - "geographic gossip efficient averaging for sensor 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.IT(Information Theory)