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: "convex approaches to model wavelet sparsity patterns"
  Abstract: Statistical dependencies among wavelet coefficients are commonly represented by graphical models such as hidden Markov trees(HMTs). However, in linear inverse problems such as deconvolution, tomography, and compressed sensing, the presence of a sensing or observation matrix produces a linear mixing of the simple Markovian dependency structure. This leads to reconstruction problems that are non-convex optimizations. Past work has dealt with this issue by resorting to greedy or suboptimal iterative reconstruction methods. In this paper, we propose new modeling approaches based on group-sparsity penalties that leads to convex optimizations that can be solved exactly and efficiently. We show that the methods we develop perform significantly better in deconvolution and compressed sensing applications, while being as computationally efficient as standard coefficient-wise approaches such as lasso.

Connected through hyperedge HE-1 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "structured sparsity discrete and convex approaches"):
  with members:
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "group sparse model selection hardness and relaxations"
    - "a novel sparsity and clustering regularization"
    - "on model based rip 1 matrices"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-3 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "convex relaxations of structured matrix factorizations"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"

Connected through hyperedge HE-4 (induced by source paper "forest sparsity for multi channel compressive sensing"):
  with members:
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-5 (induced by source paper "classification with sparse overlapping groups"):
  with members:
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "structured sparsity and generalization"
    - "collaborative hierarchical sparse modeling"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-6 (induced by source paper "translation invariant shrinkage thresholding of group sparse signals"):
  with members:
    - "sparse signal estimation by maximally sparse convex optimization"
    - "structured sparsity through convex optimization"

Connected through hyperedge HE-7 (induced by source paper "near optimal adaptive compressed sensing"):
  with members:
    - "the sample complexity of search over multiple populations"
    - "near optimal compressive binary search"
    - "boolean compressed sensing and noisy group testing"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "sequential testing for sparse recovery"
    - "on the limits of sequential testing in high dimensions"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "group sparse model selection hardness and relaxations"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "total variation regularization for fmri based prediction of behavior"
    - "model based 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.CV(Computer Vision and Pattern Recognition)