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: "sparse volterra and polynomial regression models recoverability and estimation"
  Abstract: Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings, can offer a viable alternative. The viability of CS for sparse Volterra and polynomial models is the core theme of this work. A common sparse regression task is initially posed for the two models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type algorithm is developed for sparse polynomial regressions. The identifiability of polynomial models is critically challenged by dimensionality. However, following the CS principle, when these models are sparse, they could be recovered by far fewer measurements. To quantify the sufficient number of measurements for a given level of sparsity, restricted isometry properties (RIP) are investigated in commonly met polynomial regression settings, generalizing known results for their linear counterparts. The merits of the novel (weighted) adaptive CS algorithms to sparse polynomial modeling are verified through synthetic as well as real data tests for genotype-phenotype analysis.

Connected through hyperedge HE-1 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "empirical bayes and full bayes for signal estimation"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "sparse recovery based on q ratio constrained minimal singular values"):
  with members:
    - "stable and robust ell_p constrained compressive sensing recovery via robust width property"
    - "unknown sparsity in compressed sensing denoising and inference"
    - "on robust width property for lasso and dantzig selector"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "estimating unknown sparsity in compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "performance analysis of sparse recovery based on constrained minimal singular values"

Connected through hyperedge HE-3 (induced by source paper "non linear system modelling based on constrained volterra series estimates"):
  with members:
    - "parametric identification of parallel wiener hammerstein systems"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-4 (induced by source paper "algorithms for learning sparse additive models with interactions in high dimensions"):
  with members:
    - "learning sparse additive models with interactions in high dimensions"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "predicting nearly as well as the optimal twice differentiable regressor"):
  with members:
    - "a comprehensive approach to universal piecewise nonlinear regression based on trees"

Connected through hyperedge HE-6 (induced by source paper "performance analysis of sparse recovery based on constrained minimal singular values"):
  with members:
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "performance analysis for sparse support recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "a direct formulation for sparse pca using semidefinite programming"

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.LG(Machine Learning)