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: "layer specific adaptive learning rates for deep networks"
  Abstract: The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to vanishing gradients, in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for shallow layers (near the input layer); this results in slow learning in the shallow layers. Additionally, it has also been shown that in highly non-convex problems, such as deep neural networks, there is a proliferation of high-error low curvature saddle points, which slows down learning dramatically. In this paper, we attempt to overcome the two above problems by proposing an optimization method for training deep neural networks which uses learning rates which are both specific to each layer in the network and adaptive to the curvature of the function, increasing the learning rate at low curvature points. This enables us to speed up learning in the shallow layers of the network and quickly escape high-error low curvature saddle points. We test our method on standard image classification datasets such as MNIST, CIFAR10 and ImageNet, and demonstrate that our method increases accuracy as well as reduces the required training time over standard algorithms.

Connected through hyperedge HE-1 (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"
    - "normalized direction preserving adam"
    - "scaling neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "blockwise adaptivity faster training and better generalization in deep learning"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "on the convergence of weighted adagrad with momentum for training deep neural networks"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "weighted adagrad with unified momentum"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "partitioned integrators for thermodynamic parameterization of neural networks"):
  with members:
    - "lca loss change allocation for neural network training"
    - "understanding generalization through visualizations"
    - "three factors influencing minima in sgd"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "improving palliative care with deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "an empirical analysis of the optimization of deep network loss surfaces"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "towards white box benchmarks for algorithm control"):
  with members:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "rllib abstractions for distributed reinforcement learning"
    - "learning to learn by gradient descent by gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "learning to optimize"
    - "asynchronous methods for deep reinforcement learning"
    - "the configurable sat solver challenge cssc"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "visual object tracking based on adaptive siamese and motion estimation network"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "vital visual tracking via adversarial learning"
    - "end to end representation learning for correlation filter based tracking"
    - "siamese learning visual tracking a survey"
    - "crest convolutional residual learning for visual tracking"
    - "robust visual tracking via hierarchical convolutional features"
    - "sanet structure aware network for visual tracking"

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.CV(Computer Vision and Pattern Recognition)