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: "performance trade offs in multi processor approximate message passing"
  Abstract: We consider large-scale linear inverse problems in Bayesian settings. Our general approach follows a recent line of work that applies the approximate message passing (AMP) framework in multi-processor (MP) computational systems by storing and processing a subset of rows of the measurement matrix along with corresponding measurements at each MP node. 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. There is a trade-off between the physical costs of the reconstruction process including computation time, communication loads, and the reconstruction quality, and it is impossible to simultaneously minimize all the costs. We pose this minimization as a multi-objective optimization problem (MOP), and study the properties of the best trade-offs (Pareto optimality) in this MOP. We prove that the achievable region of this MOP is convex, and conjecture how the combined cost of computation and communication scales with the desired mean squared error. These properties are verified numerically.

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"
    - "optimal trade offs in multi processor approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo"):
  with members:
    - "gaussian message passing for overloaded massive mimo noma"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
    - "decentralized baseband processing for massive mu mimo systems"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-3 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"
    - "two part reconstruction with noisy sudocodes"

Connected through hyperedge HE-4 (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"
    - "optimal trade offs in multi processor approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "multi processor approximate message passing using lossy compression"

Connected through hyperedge HE-5 (induced by source paper "on the achievable rates of decentralized equalization in massive mu mimo systems"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "optimality of large mimo detection via approximate message passing"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "massive mimo how many antennas do we need"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "multiprocessor approximate message passing with column wise partitioning"):
  with members:
    - "optimal 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-8 (induced by source paper "optimal trade offs in multi processor approximate message passing"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "hybrid approximate message passing"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "multi processor approximate message passing using lossy compression"
    - "mismatched estimation in large linear systems"
    - "signal estimation with additive error metrics in compressed sensing"
    - "distributed compressed sensing for static and time varying networks"
    - "performance regions in compressed sensing from noisy measurements"

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)