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: "exploring loss function topology with cyclical learning rates"
  Abstract: We present observations and discussion of previously unreported phenomena discovered while training residual networks. The goal of this work is to better understand the nature of neural networks through the examination of these new empirical results. These behaviors were identified through the application of Cyclical Learning Rates (CLR) and linear network interpolation. Among these behaviors are counterintuitive increases and decreases in training loss and instances of rapid training. For example, we demonstrate how CLR can produce greater testing accuracy than traditional training despite using large learning rates. Files to replicate these results are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "a simple dynamic learning rate tuning algorithm for automated training of dnns"):
  with members:
    - "online learning rate adaptation with hypergradient descent"
    - "don t decay the learning rate increase the batch size"
    - "improved regularization of convolutional neural networks with cutout"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "sgdr stochastic gradient descent with restarts"
    - "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
    - "densely connected convolutional networks"
    - "lets keep it simple using simple architectures to outperform deeper architectures"

Connected through hyperedge HE-2 (induced by source paper "visualizing movement control optimization landscapes"):
  with members:
    - "deepmind control suite"
    - "learning symmetric and low energy locomotion"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "unity a general platform for intelligent agents"
    - "visualizing the loss landscape of neural nets"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "sharp minima can generalize for deep nets"
    - "learning locomotion skills using deeprl does the choice of action space matter"

Connected through hyperedge HE-3 (induced by source paper "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "on calibration of modern neural networks"
    - "molecular de novo design through deep reinforcement learning"
    - "confidence estimation in deep neural networks via density modelling"
    - "low data drug discovery with one shot learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "towards open set deep networks"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-4 (induced by source paper "reliable prediction errors for deep neural networks using test time dropout"):
  with members:
    - "kekulescope improved prediction of cancer cell line sensitivity using convolutional neural networks trained on compound images"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "want drugs use python"
    - "cyclical learning rates for training neural networks"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-5 (induced by source paper "averaging weights leads to wider optima and better generalization"):
  with members:
    - "sharp minima can generalize for deep nets"
    - "shake shake regularization"
    - "sgdr stochastic gradient descent with restarts"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "deep pyramidal residual networks"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-6 (induced by source paper "visualizing the loss landscape of neural nets"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "depth creates no bad local minima"
    - "the loss surface of deep and wide neural networks"
    - "global optimality conditions for deep neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "accurate large minibatch sgd training imagenet in 1 hour"

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)