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 limits for noisy multimeasurement vector problems"
  Abstract: Compressed sensing (CS) demonstrates that sparse signals can be estimated from underdetermined linear systems. Distributed CS (DCS) further reduces the number of measurements by considering joint sparsity within signal ensembles. DCS with jointly sparse signals has applications in multisensor acoustic sensing, magnetic resonance imaging with multiple coils, remote sensing, and array signal processing. Multimeasurement vector (MMV) problems consider the estimation of jointly sparse signals under the DCS framework. Two related MMV settings are studied. In the first setting, each signal vector is measured by a different independent and identically distributed (i.i.d.) measurement matrix, while in the second setting, all signal vectors are measured by the same i.i.d. matrix. Replica analysis is performed for these two MMV settings, and the minimum mean squared error (MMSE), which turns out to be identical for both settings, is obtained as a function of the noise variance and number of measurements. To showcase the application of MMV models, the MMSE's of complex CS problems with both real and complex measurement matrices are also analyzed. Multiple performance regions for MMV are identified where the MMSE behaves differently as a function of the noise variance and the number of measurements. Belief propagation (BP) is a CS signal estimation framework that often achieves the MMSE asymptotically. A phase transition for BP is identified. This phase transition, verified by numerical results, separates the regions where BP achieves the MMSE and where it is suboptimal. Numerical results also illustrate that more signal vectors in the jointly sparse signal ensemble lead to a better phase transition.

Connected through hyperedge HE-1 (induced by source paper "exploiting prior information in block sparse signals"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "spectral super resolution with prior knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "recovering compressively sampled signals using partial support information"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (induced by source paper "the committee machine computational to statistical gaps in learning a two layers neural network"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "decoding from pooled data phase transitions of message passing"
    - "spurious local minima are common in two layer relu neural networks"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-3 (induced by source paper "theoretical bounds on map estimation in distributed sensing networks"):
  with members:
    - "performance analysis of approximate message passing for distributed compressed sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"
    - "statistical mechanics of map estimation general replica ansatz"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "performance analysis of approximate message passing for distributed compressed sensing"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "omp based joint sparsity pattern recovery under communication constraints"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "source coding optimization for distributed average consensus"):
  with members:
    - "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"
    - "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"

Connected through hyperedge HE-6 (induced by source paper "on collaborative compressive sensing systems the framework design and algorithm"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "sparsity averaging for compressive imaging"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "sparse signal reconstruction via iterative support detection"
    - "distributed compressive 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)