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: "an overview of multi processor approximate message passing"
  Abstract: Approximate message passing (AMP) is an algorithmic framework for solving linear inverse problems from noisy measurements, with exciting applications such as reconstructing images, audio, hyper spectral images, and various other signals, including those acquired in compressive signal acquisiton systems. The growing prevalence of big data systems has increased interest in large-scale problems, which may involve huge measurement matrices that are unsuitable for conventional computing systems. To address the challenge of large-scale processing, multiprocessor (MP) versions of AMP have been developed. We provide an overview of two such MP-AMP variants. In row-MP-AMP, each computing node stores a subset of the rows of the matrix and processes corresponding measurements. In column- MP-AMP, each node stores a subset of columns, and is solely responsible for reconstructing a portion of the signal. We will discuss pros and cons of both approaches, summarize recent research results for each, and explain when each one may be a viable approach. Aspects that are highlighted include some recent results on state evolution for both MP-AMP algorithms, and the use of data compression to reduce communication in the MP network.

Connected through hyperedge HE-1 (induced by source paper "source coding optimization for distributed average consensus"):
  with members:
    - "performance limits for noisy multimeasurement vector problems"
    - "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"
    - "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"

Connected through hyperedge HE-2 (induced by source paper "generalized geometric programming for rate allocation in consensus"):
  with members:
    - "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"
    - "network based consensus averaging with general noisy channels"

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)