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: "convolutional compressed sensing using deterministic sequences"
  Abstract: In this paper, a new class of orthogonal circulant matrices built from deterministic sequences is proposed for convolution-based compressed sensing (CS). In contrast to random convolution, the coefficients of the underlying filter are given by the discrete Fourier transform of a deterministic sequence with good autocorrelation. Both uniform recovery and non-uniform recovery of sparse signals are investigated, based on the coherence parameter of the proposed sensing matrices. Many examples of the sequences are investigated, particularly the Frank-Zadoff-Chu (FZC) sequence, the m-sequence and the Golay sequence. A salient feature of the proposed sensing matrices is that they can not only handle sparse signals in the time domain, but also those in the frequency and/or or discrete-cosine transform (DCT) domain.

Connected through hyperedge HE-1 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "a novel millimeter wave channel simulator and applications for 5g wireless communications"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-2 (induced by source paper "deterministic partial binary circulant compressed sensing matrices"):
  with members:
    - "quantization of compressive samples with stable and robust recovery"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices in compressed sensing"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"

Connected through hyperedge HE-4 (induced by source paper "uniform recovery bounds for structured random matrices in corrupted compressed sensing"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "modulated unit norm tight frames for compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the restricted isometry property for random block diagonal matrices"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"

Connected through hyperedge HE-5 (induced by source paper "convcsnet a convolutional compressive sensing framework based on deep learning"):
  with members:
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "a deep learning approach to block based compressed sensing of images"
    - "a deep learning approach to structured signal recovery"
    - "accurate image super resolution using very deep convolutional networks"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "from denoising to compressed sensing"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "state of the art and prospects of structured sensing matrices in compressed sensing"):
  with members:
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "structured compressed sensing from theory to applications"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "deterministic compressed sensing matrices from multiplicative character sequences"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-7 (induced by source paper "modulated unit norm tight frames for compressed sensing"):
  with members:
    - "sparsity averaging for compressive imaging"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "the restricted isometry property for random block diagonal matrices"
    - "spread spectrum magnetic resonance imaging"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-8 (induced by source paper "structured random measurements in signal processing"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "a partial derandomization of phaselift using spherical designs"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "phase retrieval from coded diffraction patterns"
    - "compressive multiplexing of correlated signals"
    - "sparse signal recovery from quadratic measurements via convex 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.IT(Information Theory)