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: "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
  Abstract: The primary challenge in linear inverse problems is to design stable and robust "decoders" to reconstruct high-dimensional vectors from a low-dimensional observation through a linear operator. Sparsity, low-rank, and related assumptions are typically exploited to design decoders which performance is then bounded based on some measure of deviation from the idealized model, typically using a norm. This paper focuses on characterizing the fundamental performance limits that can be expected from an ideal decoder given a general model, ie, a general subset of "simple" vectors of interest. First, we extend the so-called notion of instance optimality of a decoder to settings where one only wishes to reconstruct some part of the original high dimensional vector from a low-dimensional observation. This covers practical settings such as medical imaging of a region of interest, or audio source separation when one is only interested in estimating the contribution of a specific instrument to a musical recording. We define instance optimality relatively to a model much beyond the traditional framework of sparse recovery, and characterize the existence of an instance optimal decoder in terms of joint properties of the model and the considered linear operator. Noiseless and noise-robust settings are both considered. We show somewhat surprisingly that the existence of noise-aware instance optimal decoders for all noise levels implies the existence of a noise-blind decoder. A consequence of our results is that for models that are rich enough to contain an orthonormal basis, the existence of an L2/L2 instance optimal decoder is only possible when the linear operator is not substantially dimension-reducing. This covers well-known cases (sparse vectors, low-rank matrices) as well as a number of seemingly new situations (structured sparsity and sparse inverse covariance matrices for instance). We exhibit an operator-dependent norm which, under a model-specific generalization of the Restricted Isometry Property (RIP), always yields a feasible instance optimality and implies instance optimality property. This norm can be upper bounded by an atomic norm relative to the considered model.

Connected through hyperedge HE-1 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "sketching for large scale learning of mixture models"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "compressive k means"
    - "adapting to unknown noise level in sparse deconvolution"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"

Connected through hyperedge HE-2 (induced by source paper "instance optimal decoding and the restricted isometry property"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "representation and coding of signal geometry"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "optimality of 1 norm regularization among weighted 1 norms for sparse recovery a case study on how to find optimal regularizations"):
  with members:
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "living on the edge phase transitions in convex programs with random data"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-4 (induced by source paper "compressed sensing in hilbert spaces"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "compressive k means"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"):
  with members:
    - "uniform recovery from subgaussian multi sensor measurements"
    - "compressed sensing and parallel acquisition"
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "refined analysis of sparse mimo radar"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"

Connected through hyperedge HE-6 (induced by source paper "sketching for large scale learning of mixture models"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "a la carte learning fast kernels"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "orthogonal matching pursuit with replacement"

Connected through hyperedge HE-7 (induced by source paper "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"):
  with members:
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "uniform recovery of fusion frame structured sparse signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "stable restoration and separation of approximately sparse signals"

Connected through hyperedge HE-8 (induced by source paper "recipes for stable linear embeddings from hilbert spaces to ℝ m"):
  with members:
    - "sketching for simultaneously sparse and low rank covariance matrices"
    - "isometric sketching of any set via the restricted isometry property"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "compressed subspace matching on the continuum"
    - "low rank matrix recovery from rank one measurements"
    - "uniform recovery of fusion frame structured sparse signals"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "breaking the coherence barrier a new theory for compressed 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)