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: "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
  Abstract: Several recent trends in machine learning theory and practice, from the design of state-of-the-art Gaussian Process to the convergence analysis of deep neural nets (DNNs) under stochastic gradient descent (SGD), have found it fruitful to study wide random neural networks. Central to these approaches are certain scaling limits of such networks. We unify these results by introducing a notion of a straightline \emph{tensor program} that can express most neural network computations, and we characterize its scaling limit when its tensors are large and randomized. From our framework follows (1) the convergence of random neural networks to Gaussian processes for architectures such as recurrent neural networks, convolutional neural networks, residual networks, attention, and any combination thereof, with or without batch normalization; (2) conditions under which the \emph{gradient independence assumption} -- that weights in backpropagation can be assumed to be independent from weights in the forward pass -- leads to correct computation of gradient dynamics, and corrections when it does not; (3) the convergence of the Neural Tangent Kernel, a recently proposed kernel used to predict training dynamics of neural networks under gradient descent, at initialization for all architectures in (1) without batch normalization. Mathematically, our framework is general enough to rederive classical random matrix results such as the semicircle and the Marchenko-Pastur laws, as well as recent results in neural network Jacobian singular values. We hope our work opens a way toward design of even stronger Gaussian Processes, initialization schemes to avoid gradient explosion/vanishing, and deeper understanding of SGD dynamics in modern architectures.

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"
    - "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"
    - "can sgd learn recurrent neural networks with provable generalization"

Connected through hyperedge HE-2 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "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"
    - "is deeper better only when shallow is good"

Connected through hyperedge HE-3 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "efficient uncertainty estimation for semantic segmentation in videos"

Connected through hyperedge HE-4 (induced by source paper "avoiding kernel fixed points computing with elu and gelu infinite networks"):
  with members:
    - "richer priors for infinitely wide multi layer perceptrons"
    - "exchangeability and kernel invariance in trained mlps"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "searching for activation functions"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "invariance of weight distributions in rectified mlps"

Connected through hyperedge HE-5 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization"):
  with members:
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "asymptotics of wide networks from feynman diagrams"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "on exact computation with an infinitely wide neural net"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-6 (induced by source paper "multi source deep gaussian process kernel learning"):
  with members:
    - "interpretable deep gaussian processes with moments"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "deep multi fidelity gaussian processes"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "deep kernel learning"
    - "generalized denoising auto encoders as generative models"
    - "gaussian processes for big data"
    - "what regularized auto encoders learn from the data generating distribution"

Connected through hyperedge HE-7 (induced by source paper "residual tangent kernels"):
  with members:
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "finite depth and width corrections to the neural tangent kernel"
    - "on exact computation with an infinitely wide neural net"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "mean field residual networks on the edge of chaos"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu networks"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "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"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural 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.NE(Neural and Evolutionary Computing)