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: "why does deep learning work a perspective from group theory"
  Abstract: Why does Deep Learning work? What representations does it capture? How do higher-order representations emerge? We study these questions from the perspective of group theory, thereby opening a new approach towards a theory of Deep learning. #R##N#One factor behind the recent resurgence of the subject is a key algorithmic step called pre-training: first search for a good generative model for the input samples, and repeat the process one layer at a time. We show deeper implications of this simple principle, by establishing a connection with the interplay of orbits and stabilizers of group actions. Although the neural networks themselves may not form groups, we show the existence of {\em shadow} groups whose elements serve as close approximations. #R##N#Over the shadow groups, the pre-training step, originally introduced as a mechanism to better initialize a network, becomes equivalent to a search for features with minimal orbits. Intuitively, these features are in a way the {\em simplest}. Which explains why a deep learning network learns simple features first. Next, we show how the same principle, when repeated in the deeper layers, can capture higher order representations, and why representation complexity increases as the layers get deeper.

Connected through hyperedge HE-1 (induced by source paper "short sighted deep learning"):
  with members:
    - "is deep learning an rg flow"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "is deep learning an rg flow"):
  with members:
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (induced by source paper "understanding trained cnns by indexing neuron selectivity"):
  with members:
    - "deepminer discovering interpretable representations for mammogram classification and explanation"
    - "revisiting the importance of individual units in cnns via ablation"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "interpreting deep visual representations via network dissection"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-4 (induced by source paper "on the selective and invariant representation of dcnn for high resolution remote sensing image recognition"):
  with members:
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "deep neural networks predict hierarchical spatio temporal cortical dynamics of human visual object recognition"
    - "learning multi scale deep features for high resolution satellite image classification"
    - "understanding neural networks through deep visualization"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "convergent learning do different neural networks learn the same representations"
    - "understanding intra class knowledge inside cnn"

Connected through hyperedge HE-5 (induced by source paper "how convnets model non linear transformations"):
  with members:
    - "global optimality in tensor factorization deep learning and beyond"
    - "on the expressive power of deep learning a tensor analysis"
    - "on the expressive efficiency of sum product networks"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "learning stable group invariant representations with convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "convergent learning do different neural networks learn the same representations"):
  with members:
    - "understanding neural networks through deep visualization"
    - "understanding deep image representations by inverting them"
    - "how transferable are features in deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "understanding image representations by measuring their equivariance and equivalence"
    - "provable bounds for learning some deep representations"

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)