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: "recovery of sparse matrices via matrix sketching"
  Abstract: In this paper, we consider the problem of recovering an unknown sparse matrix X from the matrix sketch Y = AX B^T. The dimension of Y is less than that of X, and A and B are known matrices. This problem can be solved using standard compressive sensing (CS) theory after converting it to vector form using the Kronecker operation. In this case, the measurement matrix assumes a Kronecker product structure. However, as the matrix dimension increases the associated computational complexity makes its use prohibitive. We extend two algorithms, fast iterative shrinkage threshold algorithm (FISTA) and orthogonal matching pursuit (OMP) to solve this problem in matrix form without employing the Kronecker product. While both FISTA and OMP with matrix inputs are shown to be equivalent in performance to their vector counterparts with the Kronecker product, solving them in matrix form is shown to be computationally more efficient. We show that the computational gain achieved by FISTA with matrix inputs over its vector form is more significant compared to that achieved by OMP.

Connected through hyperedge HE-1 (induced by source paper "localized compression applying convolutional neural networks to compressed images"):
  with members:
    - "a survey of model compression and acceleration for deep neural networks"
    - "densely connected convolutional networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "a tutorial on principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "sub nyquist radar principles and prototypes"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "expected rip conditioning of the modulated wideband converter"

Connected through hyperedge HE-3 (induced by source paper "sub nyquist sar via fourier domain range doppler processing"):
  with members:
    - "fast compressed sensing sar imaging based on approximated observation"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "xampling at the rate of innovation"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling bridging theory and practice"

Connected through hyperedge HE-4 (induced by source paper "summer sub nyquist mimo radar"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "analysis of sparse mimo radar"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"

Connected through hyperedge HE-5 (induced by source paper "cognitive sub nyquist hardware prototype of a collocated mimo radar"):
  with members:
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "sub nyquist radar via doppler focusing"
    - "mimo radar using compressive sampling"

Connected through hyperedge HE-6 (induced by source paper "positive definite estimation of large covariance matrix using generalized nonconvex penalties"):
  with members:
    - "massive mimo as a big data system random matrix models and testbed"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "sketching sparse matrices"

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.NA(Numerical Analysis)