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: "unsupervised feature learning by deep sparse coding"
  Abstract: In this paper, we propose a new unsupervised feature learning framework, namely Deep Sparse Coding (DeepSC), that extends sparse coding to a multi-layer architecture for visual object recognition tasks. The main innovation of the framework is that it connects the sparse-encoders from different layers by a sparse-to-dense module. The sparse-to-dense module is a composition of a local spatial pooling step and a low-dimensional embedding process, which takes advantage of the spatial smoothness information in the image. As a result, the new method is able to learn several levels of sparse representation of the image which capture features at a variety of abstraction levels and simultaneously preserve the spatial smoothness between the neighboring image patches. Combining the feature representations from multiple layers, DeepSC achieves the state-of-the-art performance on multiple object recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "structured and deep similarity matching via structured and deep hebbian networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "sparse deep predictive coding captures contour integration capabilities of the early visual system"
    - "meaningful representations emerge from sparse deep predictive coding"
    - "a spiking neural network with local learning rules derived from nonnegative similarity matching"
    - "deep sparse coding for invariant multimodal halle berry neurons"
    - "online representation learning with single and multi layer hebbian networks for image classification"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-2 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"

Connected through hyperedge HE-3 (induced by source paper "supervised deep sparse coding networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "deep ten texture encoding network"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "wide residual networks"
    - "swapout learning an ensemble of deep architectures"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "scatternet hybrid deep learning shdl network for object classification"):
  with members:
    - "dual tree wavelet scattering network with parametric log transformation for object classification"
    - "unsupervised high level feature learning by ensemble projection for semi supervised image classification and image clustering"
    - "improved techniques for training gans"
    - "wide residual networks"
    - "multi resolution dual tree wavelet scattering network for signal classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

Connected through hyperedge HE-5 (induced by source paper "deep sparse coding for invariant multimodal halle berry neurons"):
  with members:
    - "dynamic routing between capsules"
    - "vqa visual question answering"
    - "multimodal sparse representation learning and applications"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "deep self taught learning for remote sensing image classification"):
  with members:
    - "densely connected convolutional networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "feedback controlled sequential lasso screening"):
  with members:
    - "screening tests for lasso problems"
    - "the symmetry of a simple optimization problem in lasso screening"
    - "deep scattering spectrum"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-8 (induced by source paper "the symmetry of a simple optimization problem in lasso screening"):
  with members:
    - "screening tests for lasso problems"
    - "feedback controlled sequential lasso screening"
    - "safe feature elimination in sparse supervised learning"

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)