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: "distributed compressive sensing"
  Abstract: Abstract : Compressive sensing is a signal acquisition framework based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable recovery. In this paper we introduce a new theory for distributed compressive sensing (DCS) that enables new distributed coding algorithms for multi-signal ensembles that exploit both intra- and inter-signal correlation structures. The DCS theory rests on a new concept that we term the joint sparsity of a signal ensemble. Our theoretical contribution is to characterize the fundamental performance limits of DCS recovery for jointly sparse signal ensembles in the noiseless measurement setting; our result connects single-signal, joint, and distributed (multi-encoder) compressive sensing. To demonstrate the efficacy of our framework and to show that additional challenges such as computational tractability can be addressed, we study in detail three example models for jointly sparse signals. For these models, we develop practical algorithms for joint recovery of multiple signals from incoherent projections. In two of our three models, the results are asymptotically best-possible, meaning that both the upper and lower bounds match the performance of our practical algorithms. Moreover, simulations indicate that the asymptotics take effect with just a moderate number of signals. DCS is immediately applicable to a range of problems in sensor arrays and networks.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (induced by source paper "distributed quantization for sparse time sequences"):
  with members:
    - "serial quantization for representing sparse signals"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "compressive sensing over networks"

Connected through hyperedge HE-3 (induced by source paper "spamhmm sparse mixture of hidden markov models for graph connected entities"):
  with members:
    - "quaternet a quaternion based recurrent model for human motion"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "802 11 wireless simulation and anomaly detection using hmm and ubm"
    - "on human motion prediction using recurrent neural networks"
    - "structural rnn deep learning on spatio temporal graphs"
    - "recurrent network models for human dynamics"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "atomic norm minimization for modal analysis from random and compressed samples"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "distributed compressed sensing off the grid"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"

Connected through hyperedge HE-5 (induced by source paper "sparse representation for wireless communications"):
  with members:
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"
    - "joint channel training and feedback for fdd massive mimo systems"
    - "compressive sensing based multiuser detector for the large scale sm mimo uplink"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "compressed channel feedback for correlated massive mimo systems"
    - "compressive sensing based differential channel feedback for massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"

Connected through hyperedge HE-6 (induced by source paper "analysis of hard thresholding for distributed compressed sensing with one bit measurements"):
  with members:
    - "recovering structured data from superimposed non linear measurements"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "the restricted isometry property for random block diagonal matrices"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-7 (induced by source paper "decentralized joint sparse signal recovery a sparse bayesian learning approach"):
  with members:
    - "decentralized subspace pursuit for joint sparsity pattern recovery"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "estimation of sparse mimo channels with common support"
    - "a fast distributed proximal gradient method"
    - "fast distributed gradient methods"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-8 (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"
    - "performance limits for noisy multimeasurement vector problems"
    - "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"

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)