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: "horizontal and vertical ensemble with deep representation for classification"
  Abstract: Representation learning, especially which by using deep learning, has been widely applied in classication. However, how to use limited size of labeled data to achieve good classication performance with deep neural network, and how can the learned features further improve classication remain indenite. In this paper, we propose Horizontal Voting Vertical Voting and Horizontal Stacked Ensemble methods to improve the classication performance of deep neural networks. In the ICML 2013 Black Box Challenge, via using these methods independently, Bing Xu achieved 3rd in public leaderboard, and 7th in private leaderboard; Jingjing Xie achieved 4th in public leaderboard, and 5th in private leaderboard.

Connected through hyperedge HE-1 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "data diversification an elegant strategy for neural machine translation"):
  with members:
    - "incorporating bert into neural machine translation"
    - "improving neural machine translation robustness via data augmentation beyond back translation"
    - "soft contextual data augmentation for neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"

Connected through hyperedge HE-3 (induced by source paper "rapid training of very large ensembles of diverse neural networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep mutual learning"
    - "snapshot ensembles train 1 get m for free"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "modularized morphing of neural networks"
    - "less is more a comprehensive framework for the number of components of ensemble classifiers"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "snapshot ensembles train 1 get m for free"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "temporal ensembling for semi supervised learning"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "swapout learning an ensemble of deep architectures"

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)