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: "efficient high dimensional inference in the multiple measurement vector problem"
  Abstract: In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection of sparse signal vectors that share a common support are recovered from undersampled noisy measurements. The algorithm, AMP-MMV, is capable of exploiting temporal correlations in the amplitudes of non-zero coefficients, and provides soft estimates of the signal vectors as well as the underlying support. Central to the proposed approach is an extension of recently developed approximate message passing techniques to the amplitude-correlated MMV setting. Aided by these techniques, AMP-MMV offers a computational complexity that is linear in all problem dimensions. In order to allow for automatic parameter tuning, an expectation-maximization algorithm that complements AMP-MMV is described. Finally, a detailed numerical study demonstrates the power of the proposed approach and its particular suitability for application to high-dimensional problems.

Connected through hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "inference with deep generative priors in high dimensions"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"
    - "task aware compressed sensing with generative adversarial networks"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"

Connected through hyperedge HE-2 (induced by source paper "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "capacity of gaussian many access channels"

Connected through hyperedge HE-3 (induced by source paper "grant free massive mtc enabled massive mimo a compressive sensing approach"):
  with members:
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a novel receiver design with joint coherent and non coherent processing"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random pilot and data access in massive mimo for machine type communications"
    - "random access protocols for massive mimo"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "design and performance analysis of noncoherent detection systems with massive receiver arrays"

Connected through hyperedge HE-4 (induced by source paper "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "compressive coded random access for massive mtc traffic in 5g systems"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "capacity of gaussian many access channels"

Connected through hyperedge HE-5 (induced by source paper "a unified bayesian inference framework for generalized linear models"):
  with members:
    - "hybrid approximate message passing"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "vector approximate message passing for the generalized linear model"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "denoising based vector approximate message passing"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-6 (induced by source paper "massive connectivity with massive mimo part i device activity detection and channel estimation"):
  with members:
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "capacity of gaussian many access channels"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"
    - "massive mimo for next generation wireless systems"

Connected through hyperedge HE-7 (induced by source paper "sparse activity detection for massive connectivity"):
  with members:
    - "capacity of gaussian many access channels"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"
    - "what will 5g be"
    - "belief propagation for joint sparse recovery"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "user activity detection in massive random access compressed sensing vs coded slotted aloha"
    - "performance analysis of approximate message passing for distributed compressed sensing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "sparse multinomial logistic regression via approximate message passing"
    - "signal estimation with additive error metrics in compressed sensing"
    - "performance regions in compressed sensing from noisy measurements"
    - "subspace methods for joint sparse recovery"

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)