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: "optimal trade offs in multi processor approximate message passing"
  Abstract: We consider large-scale linear inverse problems in Bayesian settings. We follow a recent line of work that applies the approximate message passing (AMP) framework to multi-processor (MP) computational systems, where each processor node stores and processes a subset of rows of the measurement matrix along with corresponding measurements. In each MP-AMP iteration, nodes of the MP system and its fusion center exchange lossily compressed messages pertaining to their estimates of the input. In this setup, we derive the optimal per-iteration coding rates using dynamic programming. We analyze the excess mean squared error (EMSE) beyond the minimum mean squared error (MMSE), and prove that, in the limit of low EMSE, the optimal coding rates increase approximately linearly per iteration. Additionally, we obtain that the combined cost of computation and communication scales with the desired estimation quality according to $O(\log^2(1/\text{EMSE}))$. Finally, we study trade-offs between the physical costs of the estimation process including computation time, communication loads, and the estimation quality as a multi-objective optimization problem, and characterize the properties of the Pareto optimal surfaces.

Connected through hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information"):
  with members:
    - "an analysis of state evolution for approximate message passing with side information"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"

Connected through hyperedge HE-2 (induced by source paper "an overview of multi processor approximate message passing"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "multiprocessor approximate message passing with column wise partitioning"
    - "hybrid approximate message passing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"

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"
    - "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-4 (induced by source paper "generalized geometric programming for rate allocation in consensus"):
  with members:
    - "an overview of 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"

Connected through hyperedge HE-5 (induced by source paper "multiprocessor approximate message passing with column wise partitioning"):
  with members:
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-6 (induced by source paper "performance trade offs in multi processor approximate message passing"):
  with members:
    - "multi processor approximate message passing using lossy compression"
    - "mismatched estimation in large linear systems"
    - "two part reconstruction with noisy sudocodes"
    - "distributed compressed sensing for static and time varying networks"
    - "performance regions in compressed sensing from noisy measurements"
    - "distributed sparse signal recovery for sensor networks"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed 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.IT(Information Theory)