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: "provable bounds for learning some deep representations"
  Abstract: We give algorithms with provable guarantees that learn a class of deep nets in the generative model view popularized by Hinton and others. Our generative model is an $n$ node multilayer neural net that has degree at most $n^{\gamma}$ for some $\gamma <1$ and each edge has a random edge weight in $[-1,1]$. Our algorithm learns {\em almost all} networks in this class with polynomial running time. The sample complexity is quadratic or cubic depending upon the details of the model. #R##N#The algorithm uses layerwise learning. It is based upon a novel idea of observing correlations among features and using these to infer the underlying edge structure via a global graph recovery procedure. The analysis of the algorithm reveals interesting structure of neural networks with random edge weights.

Connected through hyperedge HE-1 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation"):
  with members:
    - "high fidelity extraction of neural network models"
    - "learning two layer rectified neural networks in polynomial time"
    - "learning two layer neural networks with symmetric inputs"
    - "adversarial attacks on neural network policies"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "electron proton dynamics in deep learning"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervisedly learned representations should the quest be over"):
  with members:
    - "aggregated residual transformations for deep neural networks"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "unsupervised learning of visual representations using videos"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-4 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-5 (induced by source paper "recursive sketches for modular deep learning"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "regularized evolution for image classifier architecture search"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "recovery guarantees for one hidden layer neural networks"
    - "learning to reason end to end module networks for visual question answering"
    - "dynamic routing between capsules"
    - "electron proton dynamics in deep learning"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-7 (induced by source paper "full stack filters to build minimum viable cnns"):
  with members:
    - "learning implicitly recurrent cnns through parameter sharing"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "slimmable neural networks"
    - "constructing fast network through deconstruction of convolution"
    - "channelnets compact and efficient convolutional neural networks via channel wise convolutions"
    - "rethinking the value of network pruning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-8 (induced by source paper "mean field limit of the learning dynamics of multilayer neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "the loss landscape of overparameterized neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

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)