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: "early stopping without a validation set"
  Abstract: Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. We propose a novel early stopping criterion based on fast-to-compute local statistics of the computed gradients and entirely removes the need for a held-out validation set. Our experiments show that this is a viable approach in the setting of least-squares and logistic regression, as well as neural networks.

Connected through hyperedge HE-1 (induced by source paper "on the efficacy of knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "on calibration of modern neural networks"
    - "deep mutual learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-2 (induced by source paper "towards realistic practices in low resource natural language processing the development set"):
  with members:
    - "imitation learning for neural morphological string transduction"
    - "bootstrapping transliteration with constrained discovery for low resource languages"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "diverse few shot text classification with multiple metrics"
    - "one shot neural cross lingual transfer for paradigm completion"

Connected through hyperedge HE-3 (induced by source paper "skeptical deep learning with distribution correction"):
  with members:
    - "deep learning is robust to massive label noise"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "temporal ensembling for semi supervised learning"
    - "wide residual networks"
    - "learning with symmetric label noise the importance of being unhinged"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "implementation of deep convolutional neural network in multi class categorical image classification"):
  with members:
    - "feed forward and backward run in deep convolution neural network"
    - "regularization and optimization strategies in deep convolutional neural network"
    - "hyperparameters optimization in deep convolutional neural network bayesian approach with gaussian process prior"
    - "deep networks with stochastic depth"
    - "deep residual learning for image recognition"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "regularization and optimization strategies in deep convolutional neural network"):
  with members:
    - "feed forward and backward run in deep convolution neural network"
    - "deep networks with stochastic depth"
    - "deep residual learning for image recognition"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)