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: "multilayer convolutional sparse modeling pursuit and dictionary learning"
  Abstract: The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse layers, provides a new interpretation of convolutional neural networks (CNNs). Under this framework, the forward pass in a CNN is equivalent to a pursuit algorithm aiming to estimate the nested sparse representation vectors from a given input signal. Despite having served as a pivotal connection between CNNs and sparse modeling, a deeper understanding of the ML-CSC is still lacking. In this paper, we propose a sound pursuit algorithm for the ML-CSC model by adopting a projection approach. We provide new and improved bounds on the stability of the solution of such pursuit and we analyze different practical alternatives to implement this in practice. We show that the training of the filters is essential to allow for nontrivial signals in the model, and we derive an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers. Last, but not least, we demonstrate the applicability of the ML-CSC model for several applications in an unsupervised setting, providing competitive results. Our work represents a bridge between matrix factorization, sparse dictionary learning, and sparse autoencoders, and we analyze these connections in detail.

Connected through hyperedge HE-1 (induced by source paper "algorithmic guarantees for inverse imaging with untrained network priors"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "deep compressed sensing"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "alternating phase projected gradient descent with generative priors for solving compressive phase retrieval"
    - "one dimensional deep image prior for time series inverse problems"
    - "audio denoising with deep network priors"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "deep ptych subsampled fourier ptychography using generative priors"

Connected through hyperedge HE-2 (induced by source paper "deep interpretable non rigid structure from motion"):
  with members:
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "single image 3d interpreter network"
    - "shapenet an information rich 3d model repository"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-3 (induced by source paper "deep network classification by scattering and homotopy dictionary learning"):
  with members:
    - "deep dictionary learning a parametric network approach"
    - "scattering networks for hybrid representation learning"
    - "kymatio scattering transforms in python"
    - "scaling the scattering transform deep hybrid networks"
    - "supervised deep sparse coding networks"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "deep roto translation scattering for object classification"

Connected through hyperedge HE-4 (induced by source paper "deep non rigid structure from motion"):
  with members:
    - "estimating depth from rgb and sparse sensing"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "parse geometry from a line monocular depth estimation with partial laser observation"
    - "single image 3d interpreter network"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "shapenet an information rich 3d model repository"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-5 (induced by source paper "convolutional dictionary learning in hierarchical networks"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-6 (induced by source paper "effect of top down connections in hierarchical sparse coding"):
  with members:
    - "deep predictive coding network for object recognition"
    - "deep predictive coding network with local recurrent processing for object recognition"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "winner take all autoencoders"
    - "k sparse autoencoders"
    - "convolutional matching pursuit and dictionary training"

Connected through hyperedge HE-7 (induced by source paper "sparse deep predictive coding captures contour integration capabilities of the early visual system"):
  with members:
    - "deep predictive coding network for object recognition"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "adam a method for stochastic optimization"
    - "k sparse autoencoders"
    - "convolutional matching pursuit and dictionary training"

Connected through hyperedge HE-8 (induced by source paper "accelerating convolutional neural networks via activation map compression"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "dnn feature map compression using learned representation over gf 2"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "weightless lossy weight encoding for deep neural network compression"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

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)