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: "on the power and limitations of random features for understanding neural networks"
  Abstract: Recently, a spate of papers have provided positive theoretical results for training over-parameterized neural networks (where the network size is larger than what is needed to achieve low error). The key insight is that with sufficient over-parameterization, gradient-based methods will implicitly leave some components of the network relatively unchanged, so the optimization dynamics will behave as if those components are essentially fixed at their initial random values. In fact, fixing these explicitly leads to the well-known approach of learning with random features. In other words, these techniques imply that we can successfully learn with neural networks, whenever we can successfully learn with random features. In this paper, we first review these techniques, providing a simple and self-contained analysis for one-hidden-layer networks. We then argue that despite the impressive positive results, random feature approaches are also inherently limited in what they can explain. In particular, we rigorously show that random features cannot be used to learn even a single ReLU neuron with standard Gaussian inputs, unless the network size (or magnitude of the weights) is exponentially large. Since a single neuron is learnable with gradient-based methods, we conclude that we are still far from a satisfying general explanation for the empirical success of neural networks.

Connected through hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "width provably matters in optimization for deep linear neural networks"
    - "finite depth and width corrections to the neural tangent kernel"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-2 (induced by source paper "taylorized training towards better approximation of neural network training at finite width"):
  with members:
    - "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
    - "beyond linearization on quadratic and higher order approximation of wide neural networks"
    - "tensor programs i wide feedforward or recurrent neural networks of any architecture are gaussian processes"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "enhanced convolutional neural tangent kernels"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning parities with neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "neural networks learning and memorization with almost no over parameterization"

Connected through hyperedge HE-4 (induced by source paper "proving the lottery ticket hypothesis pruning is all you need"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "effect of activation functions on the training of overparametrized neural nets"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "what s hidden in a randomly weighted neural network"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "complexity of training relu neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a corrective view of neural networks representation memorization and learning"):
  with members:
    - "effect of activation functions on the training of overparametrized neural nets"
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-6 (induced by source paper "training batchnorm and only batchnorm on the expressive power of random features in cnns"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a mean field theory of batch normalization"
    - "what s hidden in a randomly weighted neural network"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"

Connected through hyperedge HE-7 (induced by source paper "learning a single neuron with gradient methods"):
  with members:
    - "fitting relus via sgd and quantized sgd"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "sgd learns the conjugate kernel class of the network"

Connected through hyperedge HE-8 (induced by source paper "approximate is good enough probabilistic variants of dimensional and margin complexity"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "sgd learns the conjugate kernel class of the network"
    - "probabilistic rank and matrix rigidity"
    - "super linear gate and super quadratic wire lower bounds for depth two and depth three threshold circuits"

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)