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: "are resnets provably better than linear predictors"
  Abstract: A residual network (or ResNet) is a standard deep neural net architecture, with state-of-the-art performance across numerous applications. The main premise of ResNets is that they allow the training of each layer to focus on fitting just the residual of the previous layer's output and the target output. Thus, we should expect that the trained network is no worse than what we can obtain if we remove the residual layers and train a shallower network instead. However, due to the non-convexity of the optimization problem, it is not at all clear that ResNets indeed achieve this behavior, rather than getting stuck at some arbitrarily poor local minimum. In this paper, we rigorously prove that arbitrarily deep, nonlinear residual units indeed exhibit this behavior, in the sense that the optimization landscape contains no local minima with value above what can be obtained with a linear predictor (namely a 1-layer network). Notably, we show this under minimal or no assumptions on the precise network architecture, data distribution, or loss function used. We also provide a quantitative analysis of approximate stationary points for this problem. Finally, we show that with a certain tweak to the architecture, training the network with standard stochastic gradient descent achieves an objective value close or better than any linear predictor.

Connected through hyperedge HE-1 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks"):
  with members:
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"
    - "gradient descent finds global minima of deep neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"

Connected through hyperedge HE-2 (induced by source paper "elimination of all bad local minima in deep learning"):
  with members:
    - "effect of depth and width on local minima in deep learning"
    - "deep learning for classical japanese literature"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "deep neural networks with multi branch architectures are less non convex"
    - "a convergence theory for deep learning via over parameterization"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-3 (induced by source paper "are deep resnets provably better than linear predictors"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "exploring generalization in deep learning"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "geometric insights into the convergence of nonlinear td learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "towards characterizing divergence in deep q learning"
    - "neural temporal difference learning converges to global optima"
    - "temporal difference learning for nonlinear value function approximation in the lazy training regime"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-5 (induced by source paper "effect of depth and width on local minima in deep learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "optimization landscape and expressivity of deep cnns"
    - "the loss surface of deep and wide neural networks"
    - "diverse neural network learns true target functions"
    - "benefits of depth in neural networks"
    - "identity matters in deep learning"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-6 (induced by source paper "on the benefit of width for neural networks disappearance of bad basins"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-7 (induced by source paper "over parameterized deep neural networks have no strict local minima for any continuous activations"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "theoretical properties of the global optimizer of two layer neural network"
    - "on the power of over parametrization in neural networks with quadratic activation"

Connected through hyperedge HE-8 (induced by source paper "small nonlinearities in activation functions create bad local minima in neural networks"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "understanding the loss surface of neural networks for binary classification"
    - "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"
    - "self normalizing 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.LG(Machine Learning)