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: "compressed sensing of simultaneous low rank and joint sparse matrices"
  Abstract: In this paper we consider the problem of recovering a high dimensional data matrix from a set of incomplete and noisy linear measurements. We introduce a new model that can efficiently restrict the degrees of freedom of the problem and is generic enough to find a lot of applications, for instance in multichannel signal compressed sensing (e.g. sensor networks, hyperspectral imaging) and compressive sparse principal component analysis (s-PCA). We assume data matrices have a simultaneous low-rank and joint sparse structure, and we propose a novel approach for efficient compressed sensing (CS) of such data. Our CS recovery approach is based on a convex minimization problem that incorporates this restrictive structure by jointly regularizing the solutions with their nuclear (trace) norm and l2/l1 mixed norm. Our theoretical analysis uses a new notion of restricted isometry property (RIP) and shows that, for sampling schemes satisfying RIP, our approach can stably recover all low-rank and joint-sparse matrices. For a certain class of random sampling schemes satisfying a particular concentration bound (e.g. the subgaussian ensembles) we derive a lower bound on the number of CS measurements indicating the near-optimality of our recovery approach as well as a significant enhancement compared to the state-of-the-art. We introduce an iterative algorithm based on proximal calculus in order to solve the joint nuclear and l2/l1 norms minimization problem and, finally, we illustrate the empirical recovery phase transition of this approach by series of numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"):
  with members:
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"

Connected through hyperedge HE-2 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"):
  with members:
    - "compressive sensing of sparse tensors"
    - "separable cosparse analysis operator learning"
    - "optimized projections for compressed sensing via rank constrained nearest correlation matrix"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "iterative hard thresholding for compressed sensing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

Connected through hyperedge HE-4 (induced by source paper "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "estimation of simultaneously sparse and low rank matrices"
    - "consistency of trace norm minimization"

Connected through hyperedge HE-5 (induced by source paper "recoverability of group sparse signals from corrupted measurements via robust group lasso"):
  with members:
    - "ripless compressed sensing from anisotropic measurements"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "low rank matrix recovery from errors and erasures"
    - "a probabilistic and ripless theory of compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-6 (induced by source paper "subspace based low rank and joint sparse matrix recovery"):
  with members:
    - "sketched svd recovering spectral features from compressive measurements"

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)