human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  Abstract: The problem of $L_p$-norm constrained coding is to convert signal into code that lies inside an $L_p$-ball and most faithfully reconstructs the signal. Previous works under the name of sparse coding considered the cases of $L_0$ and $L_1$ norms. The cases with $p>1$ values, i.e. non-sparse coding studied in this paper, remain a difficulty. We propose an interpretable deep structure namely Frank-Wolfe Network (F-W Net), whose architecture is inspired by unrolling and truncating the Frank-Wolfe algorithm for solving an $L_p$-norm constrained problem with $p\geq 1$. We show that the Frank-Wolfe solver for the $L_p$-norm constraint leads to a novel closed-form nonlinear unit, which is parameterized by $p$ and termed $pool_p$. The $pool_p$ unit links the conventional pooling, activation, and normalization operations, making F-W Net distinct from existing deep networks either heuristically designed or converted from projected gradient descent algorithms. We further show that the hyper-parameter $p$ can be made learnable instead of pre-chosen in F-W Net, which gracefully solves the non-sparse coding problem even with unknown $p$. We evaluate the performance of F-W Net on an extensive range of simulations as well as the task of handwritten digit recognition, where F-W Net exhibits strong learning capability. We then propose a convolutional version of F-W Net, and apply the convolutional F-W Net into image denoising and super-resolution tasks, where F-W Net all demonstrates impressive effectiveness, flexibility, and robustness.

Includes members:
  Member "learning simple thresholded features with sparse support recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can we gain more from orthogonality regularizations in training deep cnns")
  Member "theoretical linear convergence of unfolded ista and its practical weights and thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "ista net interpretable optimization inspired deep network for image compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "a unified optimization view on generalized matching pursuit and frank wolfe":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalization guarantees for sparse kernel approximation with entropic optimal features")
  Member "maximal sparsity with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "learning optimal nonlinearities for iterative thresholding algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "normalizing the normalizers comparing and extending network normalization schemes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "extended batch normalization")

Question: Please tell me which class the source paper 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)