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: "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
  Abstract: We show that any smooth bi-Lipschitz $h$ can be represented exactly as a composition $h_m \circ ... \circ h_1$ of functions $h_1,...,h_m$ that are close to the identity in the sense that each $\left(h_i-\mathrm{Id}\right)$ is Lipschitz, and the Lipschitz constant decreases inversely with the number $m$ of functions composed. This implies that $h$ can be represented to any accuracy by a deep residual network whose nonlinear layers compute functions with a small Lipschitz constant. Next, we consider nonlinear regression with a composition of near-identity nonlinear maps. We show that, regarding Fr\'echet derivatives with respect to the $h_1,...,h_m$, any critical point of a quadratic criterion in this near-identity region must be a global minimizer. In contrast, if we consider derivatives with respect to parameters of a fixed-size residual network with sigmoid activation functions, we show that there are near-identity critical points that are suboptimal, even in the realizable case. Informally, this means that functional gradient methods for residual networks cannot get stuck at suboptimal critical points corresponding to near-identity layers, whereas parametric gradient methods for sigmoidal residual networks suffer from suboptimal critical points in the near-identity region.

Connected through hyperedge HE-1 (induced by source paper "towards understanding the importance of shortcut connections in residual networks"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "learning strict identity mappings in deep residual networks"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "visualizing the loss landscape of neural nets"
    - "super convergence very fast training of residual networks using large learning rates"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-2 (induced by source paper "gresnet graph residual network for reviving deep gnns from suspended animation"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "graph optimized convolutional networks"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "graph neural networks a review of methods and applications"
    - "norm preservation why residual networks can become extremely deep"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "invertible residual networks"

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"
    - "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"
    - "are resnets provably better than linear predictors"
    - "exploring generalization in deep learning"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "chaining meets chain rule multilevel entropic regularization and training of neural nets"):
  with members:
    - "distribution dependent analysis of gibbs erm principle"
    - "tightening mutual information based bounds on generalization error"
    - "data dependent pac bayes priors via differential privacy"
    - "generalization error bounds for noisy iterative algorithms"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"

Connected through hyperedge HE-5 (induced by source paper "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "skip connections eliminate singularities"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "electron proton dynamics in deep learning"
    - "the loss surface of deep and wide neural networks"
    - "sgd learns the conjugate kernel class of the network"

Connected through hyperedge HE-6 (induced by source paper "norm preservation why residual networks can become extremely deep"):
  with members:
    - "the singular values of convolutional layers"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "are resnets provably better than linear predictors"
    - "eraserelu a simple way to ease the training of deep convolution neural networks"
    - "skip connections eliminate singularities"
    - "the reversible residual network backpropagation without storing activations"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "density estimation using real nvp"

Connected through hyperedge HE-7 (induced by source paper "step size matters in deep learning"):
  with members:
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "residual networks lyapunov stability and convex decomposition"
    - "geometry of optimization and implicit regularization in deep learning"
    - "spectrally normalized margin bounds for neural networks"
    - "identity matters in deep learning"
    - "deep residual learning for image recognition"
    - "exact solutions to the nonlinear dynamics of learning in deep linear 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)