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: "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
  Abstract: Given a convolutional dictionary underlying a set of observed signals, can a carefully designed auto-encoder recover the dictionary in the presence of noise? We introduce an auto-encoder architecture, termed constrained recurrent sparse auto-encoder (CRsAE), that answers this question in the affirmative. Given an input signal and an approximate dictionary, the encoder finds a sparse approximation using FISTA. The decoder reconstructs the signal by applying the dictionary to the output of the encoder. The encoder and decoder in CRsAE parallel the sparse-coding and dictionary update steps in optimization-based alternating-minimization schemes for dictionary learning. As such, the parameters of the encoder and decoder are not independent, a constraint which we enforce for the first time. We derive the back-propagation algorithm for CRsAE. CRsAE is a framework for blind source separation that, only knowing the number of sources (dictionary elements), and assuming sparsely-many can overlap, is able to separate them. We demonstrate its utility in the context of spike sorting, a source separation problem in computational neuroscience. We demonstrate the ability of CRsAE to recover the underlying dictionary and characterize its sensitivity as a function of SNR.

Connected through hyperedge HE-1 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions"):
  with members:
    - "handling incomplete heterogeneous data using vaes"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "simple efficient and neural algorithms for sparse coding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "deep networks for image super resolution with sparse prior"
    - "adam a method for stochastic optimization"
    - "sparsity based poisson denoising with dictionary learning"

Connected through hyperedge HE-2 (induced by source paper "deep residual auto encoders for expectation maximization based dictionary learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "k sparse autoencoders"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-3 (induced by source paper "convolutional dictionary learning in hierarchical networks"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "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-4 (induced by source paper "randnet deep learning with compressed measurements of images"):
  with members:
    - "deep residual auto encoders for expectation maximization based dictionary learning"
    - "discriminative recurrent sparse auto encoders"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

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.LG(Machine Learning)