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: "training recurrent neural networks by diffusion"
  Abstract: This work presents a new algorithm for training recurrent neural networks (although ideas are applicable to feedforward networks as well). The algorithm is derived from a theory in nonconvex optimization related to the diffusion equation. The contributions made in this work are two fold. First, we show how some seemingly disconnected mechanisms used in deep learning such as smart initialization, annealed learning rate, layerwise pretraining, and noise injection (as done in dropout and SGD) arise naturally and automatically from this framework, without manually crafting them into the algorithms. Second, we present some preliminary results on comparing the proposed method against SGD. It turns out that the new algorithm can achieve similar level of generalization accuracy of SGD in much fewer number of epochs.

Connected through hyperedge HE-1 (induced by source paper "adaptive regularization via residual smoothing in deep learning optimization"):
  with members:
    - "combining learning rate decay and weight decay with complexity gradient descent part i"
    - "towards understanding regularization in batch normalization"
    - "understanding batch normalization"
    - "audio visual scene analysis with self supervised multisensory features"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "unsupervised learning of depth and ego motion from video"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-2 (induced by source paper "detecting synapse location and connectivity by signed proximity estimation and pruning with deep nets"):
  with members:
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "a deep structured learning approach towards automating connectome reconstruction from 3d electron micrographs"
    - "mask r cnn"
    - "fully automatic synapse prediction and validation on a large data set"
    - "flood filling networks"
    - "sshmt semi supervised hierarchical merge tree for electron microscopy image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "kickstarting deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep relaxation partial differential equations for optimizing deep neural networks"):
  with members:
    - "parle parallelizing stochastic gradient descent"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"
    - "noisy activation functions"
    - "trivializing the energy landscape of deep networks"
    - "on the energy landscape of deep networks"

Connected through hyperedge HE-5 (induced by source paper "noisy networks for exploration"):
  with members:
    - "parameter space noise for exploration"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"
    - "bayesian recurrent neural networks"
    - "deep exploration via bootstrapped dqn"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-6 (induced by source paper "empirical analysis of the hessian of over parametrized neural networks"):
  with members:
    - "three factors influencing minima in sgd"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "sharp minima can generalize for deep nets"
    - "opening the black box of deep neural networks via information"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-7 (induced by source paper "sequence stacking using dual encoder seq2seq recurrent networks"):
  with members:
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural turing machines"

Connected through hyperedge HE-8 (induced by source paper "entropy sgd biasing gradient descent into wide valleys"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "understanding deep learning requires rethinking generalization"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "scalable bayesian learning of recurrent neural networks for language modeling"
    - "recurrent batch normalization"
    - "bayesian dark knowledge"
    - "trivializing the energy landscape of deep 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)