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: "deep component analysis via alternating direction neural networks"
  Abstract: Despite a lack of theoretical understanding, deep neural networks have achieved unparalleled performance in a wide range of applications. On the other hand, shallow representation learning with component analysis is associated with rich intuition and theory, but smaller capacity often limits its usefulness. To bridge this gap, we introduce Deep Component Analysis (DeepCA), an expressive multilayer model formulation that enforces hierarchical structure through constraints on latent variables in each layer. For inference, we propose a differentiable optimization algorithm implemented using recurrent Alternating Direction Neural Networks (ADNNs) that enable parameter learning using standard backpropagation. By interpreting feed-forward networks as single-iteration approximations of inference in our model, we provide both a novel theoretical perspective for understanding them and a practical technique for constraining predictions with prior knowledge. Experimentally, we demonstrate performance improvements on a variety of tasks, including single-image depth prediction with sparse output constraints.

Connected through hyperedge HE-1 (induced by source paper "a convergence analysis of nonlinearly constrained admm in deep learning"):
  with members:
    - "fenchel lifted networks a lagrange relaxation of neural network training"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "training neural networks without gradients a scalable admm approach"
    - "constructive neural network learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "robust conditional generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generative image inpainting with contextual attention"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "mathematics of deep learning"
    - "robust imitation of diverse behaviors"
    - "unsupervised image to image translation networks"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional compressed sensing for lidar depth completion"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "sparsity invariant cnns"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual networks"
    - "semantically guided depth upsampling"
    - "atgv net accurate depth super resolution"

Connected through hyperedge HE-4 (induced by source paper "on multi layer basis pursuit efficient algorithms and convolutional neural networks"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "trainlets dictionary learning in high dimensions"
    - "densely connected convolutional networks"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"

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)