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: "l4 practical loss based stepsize adaptation for deep learning"
  Abstract: We propose a stepsize adaptation scheme for stochastic gradient descent. It operates directly with the loss function and rescales the gradient in order to make fixed predicted progress on the loss. We demonstrate its capabilities by strongly improving the performance of Adam and Momentum optimizers. The enhanced optimizers with default hyperparameters consistently outperform their constant stepsize counterparts, even the best ones, without a measurable increase in computational cost. The performance is validated on multiple architectures including ResNets and the Differential Neural Computer. A prototype implementation as a TensorFlow optimizer is released.

Connected through hyperedge HE-1 (induced by source paper "collaborative sampling in generative adversarial networks"):
  with members:
    - "collagan collaborative gan for missing image data imputation"
    - "theoretically principled trade off between robustness and accuracy"
    - "generative collaborative networks for single image super resolution"
    - "self supervised gans via auxiliary rotation loss"
    - "phase collaborative network for multi phase medical imaging segmentation"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "adversarial feedback loop"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-2 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"
    - "online learning rate adaptation with hypergradient descent"

Connected through hyperedge HE-3 (induced by source paper "training neural networks for and by interpolation"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "deep frank wolfe for neural network optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepobs a deep learning optimizer benchmark suite"):
  with members:
    - "on the convergence of adam and beyond"
    - "bda pch block diagonal approximation of positive curvature hessian for training neural networks"
    - "know what you don t know unanswerable questions for squad"
    - "block diagonal hessian free optimization for training neural networks"
    - "tbd benchmarking and analyzing deep neural network training"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "dynamic routing between capsules"

Connected through hyperedge HE-5 (induced by source paper "pal a fast dnn optimization method based on curvature information"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "shakedrop regularization for deep residual learning"
    - "shake shake regularization"
    - "neither quick nor proper evaluation of quickprop for learning deep neural networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"

Connected through hyperedge HE-6 (induced by source paper "parabolic approximation line search an efficient and effective line search approach for dnns"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "neither quick nor proper evaluation of quickprop for learning deep neural networks"
    - "probabilistic line searches for stochastic optimization"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "step size matters in deep learning"):
  with members:
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "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)