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: "relaxing tight frame condition in parallel proximal methods for signal restoration"
  Abstract: A fruitful approach for solving signal deconvolution problems consists of resorting to a frame-based convex variational formulation. In this context, parallel proximal algorithms and related alternating direction methods of multipliers have become popular optimization techniques to approximate iteratively the desired solution. Until now, in most of these methods, either Lipschitz differentiability properties or tight frame representations were assumed. In this paper, it is shown that it is possible to relax these assumptions by considering a class of non-necessarily tight frame representations, thus offering the possibility of addressing a broader class of signal restoration problems. In particular, it is possible to use non-necessarily maximally decimated filter banks with perfect reconstruction, which are common tools in digital signal processing. The proposed approach allows us to solve both frame analysis and frame synthesis problems for various noise distributions. In our simulations, it is applied to the deconvolution of data corrupted with Poisson noise or Laplacian noise by using (non-tight) discrete dual-tree wavelet representations and filter bank structures.

Connected through hyperedge HE-1 (induced by source paper "compressive deconvolution in medical ultrasound imaging"):
  with members:
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sampling and recovery of pulse streams"

Connected through hyperedge HE-2 (induced by source paper "reconstruction of enhanced ultrasound images from compressed measurements using simultaneous direction method of multipliers"):
  with members:
    - "compressive deconvolution in medical ultrasound imaging"
    - "compressive deconvolution in random mask imaging"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-3 (induced by source paper "a nonlocal structure tensor based approach for multicomponent image recovery problems"):
  with members:
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "epigraphical splitting for solving constrained convex formulations of inverse problems with proximal tools"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "epigraphical splitting for solving constrained convex formulations of inverse problems with proximal tools"):
  with members:
    - "a panorama on multiscale geometric representations intertwining spatial directional and frequency selectivity"
    - "optimization with sparsity inducing penalties"

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.OH(Other Computer Science)