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: "compressive sensing of sparse tensors"
  Abstract: Compressive sensing (CS) has triggered an enormous research activity since its first appearance. CS exploits the signal's sparsity or compressibility in a particular domain and integrates data compression and acquisition, thus allowing exact reconstruction through relatively few nonadaptive linear measurements. While conventional CS theory relies on data representation in the form of vectors, many data types in various applications, such as color imaging, video sequences, and multisensor networks, are intrinsically represented by higher order tensors. Application of CS to higher order data representation is typically performed by conversion of the data to very long vectors that must be measured using very large sampling matrices, thus imposing a huge computational and memory burden. In this paper, we propose generalized tensor compressive sensing (GTCS)-a unified framework for CS of higher order tensors, which preserves the intrinsic structure of tensor data with reduced computational complexity at reconstruction. GTCS offers an efficient means for representation of multidimensional data by providing simultaneous acquisition and compression from all tensor modes. In addition, we propound two reconstruction procedures, a serial method and a parallelizable method. We then compare the performance of the proposed method with Kronecker compressive sensing (KCS) and multiway compressive sensing (MWCS). We demonstrate experimentally that GTCS outperforms KCS and MWCS in terms of both reconstruction accuracy (within a range of compression ratios) and processing speed. The major disadvantage of our methods (and of MWCS as well) is that the compression ratios may be worse than that offered by KCS.

Connected through hyperedge HE-1 (induced by source paper "rip based performance guarantee for low tubal rank tensor recovery"):
  with members:
    - "low rank tensor recovery via iterative hard thresholding"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"

Connected through hyperedge HE-2 (induced by source paper "a compressive channel estimation technique robust to synchronization impairments"):
  with members:
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "self calibration and biconvex compressive sensing"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "28 ghz and 73 ghz millimeter wave indoor propagation measurements and path loss models"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "compressive parameter estimation in awgn"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "structured compressed sensing from theory to applications"

Connected through hyperedge HE-3 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"):
  with members:
    - "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"
    - "compressed sensing of simultaneous low rank and joint sparse 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-4 (induced by source paper "sparsity undersampling tradeoffs in anisotropic undersampling with applications in mr imaging spectroscopy"):
  with members:
    - "uniform recovery from subgaussian multi sensor measurements"
    - "living on the edge phase transitions in convex programs with random data"
    - "the restricted isometry property for random block diagonal matrices"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "total variation regularized tensor rpca for background subtraction from compressive measurements"):
  with members:
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "surveillance video processing using compressive sensing"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "online robust subspace tracking from partial information"
    - "online identification and tracking of subspaces from highly incomplete information"

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)