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: "learning one convolutional layer with overlapping patches"
  Abstract: We give the first provably efficient algorithm for learning a one hidden layer convolutional network with respect to a general class of (potentially overlapping) patches. Additionally, our algorithm requires only mild conditions on the underlying distribution. We prove that our framework captures commonly used schemes from computer vision, including one-dimensional and two-dimensional "patch and stride" convolutions. #R##N#Our algorithm-- $Convotron$ -- is inspired by recent work applying isotonic regression to learning neural networks. Convotron uses a simple, iterative update rule that is stochastic in nature and tolerant to noise (requires only that the conditional mean function is a one layer convolutional network, as opposed to the realizable setting). In contrast to gradient descent, Convotron requires no special initialization or learning-rate tuning to converge to the global optimum. #R##N#We also point out that learning one hidden convolutional layer with respect to a Gaussian distribution and just $one$ disjoint patch $P$ (the other patches may be arbitrary) is $easy$ in the following sense: Convotron can efficiently recover the hidden weight vector by updating $only$ in the direction of $P$.

Connected through hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "implicit regularization in deep matrix factorization"
    - "on the learnability of deep random networks"

Connected through hyperedge HE-2 (induced by source paper "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"):
  with members:
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural 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"

Connected through hyperedge HE-3 (induced by source paper "understanding straight through estimator in training activation quantized neural nets"):
  with members:
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "deep neural nets with interpolating function as output activation"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"

Connected through hyperedge HE-4 (induced by source paper "tight sample complexity of learning one hidden layer convolutional neural networks"):
  with members:
    - "an improved analysis of training over parameterized deep neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "generalization bounds of stochastic gradient descent for wide and deep 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"

Connected through hyperedge HE-5 (induced by source paper "time accuracy tradeoffs for learning a relu with respect to gaussian marginals"):
  with members:
    - "complexity of training relu neural networks"
    - "gradient descent for one hidden layer neural networks polynomial convergence and sq lower bounds"
    - "efficient active learning of sparse halfspaces"
    - "the computational complexity of training relu s"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "learning geometric concepts with nasty noise"
    - "learning relus via gradient descent"

Connected through hyperedge HE-6 (induced by source paper "on the learning dynamics of two layer nonlinear convolutional neural networks"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the generalization bias of two layer convolutional linear classifiers with gradient descent"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-7 (induced by source paper "fitting relus via sgd and quantized sgd"):
  with members:
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "gradiveq vector quantization for bandwidth efficient gradient aggregation in distributed cnn training"
    - "signsgd compressed optimisation for non convex problems"
    - "learning relus via gradient descent"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "learning depth three neural networks in polynomial time"

Connected through hyperedge HE-8 (induced by source paper "learning distributions generated by one layer relu networks"):
  with members:
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "learning two layer neural networks with symmetric inputs"
    - "learning relus via gradient descent"
    - "recovery guarantees for one hidden layer neural networks"
    - "convergence analysis of two layer neural networks with relu activation"
    - "pixel recurrent neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "computing a nonnegative matrix factorization provably"

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)