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: "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
  Abstract: Many inverse problems in signal processing deal with the robust estimation of unknown data from underdetermined linear observations. Low dimensional models, when combined with appropriate regularizers, have been shown to be efficient at performing this task. Sparse models with the 1-norm or low rank models with the nuclear norm are examples of such successful combinations. Stable recovery guarantees in these settings have been established using a common tool adapted to each case: the notion of restricted isometry property (RIP). In this paper, we establish generic RIP-based guarantees for the stable recovery of cones (positively homogeneous model sets) with arbitrary regularizers. These guarantees are illustrated on selected examples. For block structured sparsity in the infinite dimensional setting, we use the guarantees for a family of regularizers which efficiency in terms of RIP constant can be controlled, leading to stronger and sharper guarantees than the state of the art.

Connected through hyperedge HE-1 (induced by source paper "the importance of phase in complex compressive sensing"):
  with members:
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "uniform recovery of fusion frame structured sparse signals"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "compressive optical deflectometric tomography a constrained total variation minimization approach"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "spread spectrum compressed sensing mri using chirp radio frequency pulses"

Connected through hyperedge HE-3 (induced by source paper "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"):
  with members:
    - "linear reconstructions and the analysis of the stable sampling rate"
    - "on oracle type local recovery guarantees in compressed sensing"
    - "robustness to unknown error in sparse regularization"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "breaking the coherence barrier a new theory for compressed sensing"

Connected through hyperedge HE-4 (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"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-5 (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"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "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-6 (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"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "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-7 (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"
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"

Connected through hyperedge HE-8 (induced by source paper "breaking the coherence barrier a new theory for compressed sensing"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "lensless imaging by compressive sensing"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "an analysis of block sampling strategies in compressed sensing"
    - "communications inspired projection design with application to 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)