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: "normalized direction preserving adam"
  Abstract: Optimization algorithms for training deep models not only affects the convergence rate and stability of the training process, but are also highly related to the generalization performance of trained models. While adaptive algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in many scenarios, they often lead to worse generalization performance than SGD, when used for training deep neural networks (DNNs). In this work, we identify two problems regarding the direction and step size for updating the weight vectors of hidden units, which may degrade the generalization performance of Adam. As a solution, we propose the normalized direction-preserving Adam (ND-Adam) algorithm, which controls the update direction and step size more precisely, and thus bridges the generalization gap between Adam and SGD. Following a similar rationale, we further improve the generalization performance in classification tasks by regularizing the softmax logits. By bridging the gap between SGD and Adam, we also shed some light on why certain optimization algorithms generalize better than others.

Connected through hyperedge HE-1 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "an overview of gradient descent optimization algorithms"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-2 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-3 (induced by source paper "stochastic gradient methods with layer wise adaptive moments for training of deep networks"):
  with members:
    - "towards a human like open domain chatbot"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "transformer xl attentive language models beyond a fixed length context"
    - "on the convergence of adam and beyond"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "bag of tricks for image classification with convolutional neural networks"
    - "scaling neural machine translation"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-4 (induced by source paper "generating geological facies models with fidelity to diversity and statistics of training images using improved generative adversarial networks"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "closing the generalization gap of adaptive gradient methods in training deep neural networks"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "decoupled weight decay regularization"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "on layer level control of dnn training and its impact on generalization"):
  with members:
    - "block normalized gradient method an empirical study for training deep neural network"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "super convergence very fast training of residual networks using large learning rates"
    - "l2 regularization versus batch and weight normalization"
    - "normalized gradient with adaptive stepsize method for deep neural network training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-7 (induced by source paper "removing the feature correlation effect of multiplicative noise"):
  with members:
    - "group normalization"
    - "identity mappings in deep residual networks"
    - "recurrent dropout without memory loss"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "regularizing cnns with locally constrained decorrelations"
    - "all you need is a good init"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "a general system of differential equations to model first order adaptive algorithms"):
  with members:
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

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)