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: "discriminative recurrent sparse auto encoders"
  Abstract: We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of iterations, and connected to two linear decoders that reconstruct the input and predict its supervised classification. Training via backpropagation-through-time initially minimizes an unsupervised sparse reconstruction error; the loss function is then augmented with a discriminative term on the supervised classification. The depth implicit in the temporally-unrolled form allows the system to exhibit all the power of deep networks, while substantially reducing the number of trainable parameters. #R##N#From an initially unstructured network the hidden units differentiate into categorical-units, each of which represents an input prototype with a well-defined class; and part-units representing deformations of these prototypes. The learned organization of the recurrent encoder is hierarchical: part-units are driven directly by the input, whereas the activity of categorical-units builds up over time through interactions with the part-units. Even using a small number of hidden units per layer, discriminative recurrent sparse auto-encoders achieve excellent performance on MNIST.

Connected through hyperedge HE-1 (induced by source paper "deep learning cardiac motion analysis for human survival prediction"):
  with members:
    - "automatic 3d bi ventricular segmentation of cardiac images by a shape constrained multi task deep learning approach"
    - "learning interpretable anatomical features through deep generative models application to cardiac remodeling"
    - "deep representation learning for human motion prediction and classification"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "a survey on deep learning in medical image analysis"
    - "an uncertain future forecasting from static images using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "randnet deep learning with compressed measurements of images"):
  with members:
    - "deep residual auto encoders for expectation maximization based dictionary learning"
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-3 (induced by source paper "emergent structures and lifetime structure evolution in artificial neural networks"):
  with members:
    - "continual learning via neural pruning"
    - "understanding deep architectures using a recursive convolutional network"

Connected through hyperedge HE-4 (induced by source paper "unsupervised image decomposition in vector layers"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "faceshop deep sketch based face image editing"
    - "group normalization"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "learning implicit fields for generative shape modeling"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "deep learning for passive synthetic aperture radar"):
  with members:
    - "deep convolutional neural network for inverse problems in imaging"
    - "recurrent inference machines for solving inverse problems"
    - "learning to learn by gradient descent by gradient descent"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "a deep learning approach to block based compressed sensing of images"
    - "a deep learning approach to structured signal recovery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-6 (induced by source paper "joint shape representation and classification for detecting pdac"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deeporgan multi level deep convolutional networks for automated pancreas segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"):
  with members:
    - "cyclical learning rates for training neural networks"
    - "adam a method for stochastic optimization"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-8 (induced by source paper "deep recurrent nmf for speech separation by unfolding iterative thresholding"):
  with members:
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "theano a python framework for fast computation of mathematical expressions"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"

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)