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: "confident multiple choice learning"
  Abstract: Ensemble methods are arguably the most trustworthy techniques for boosting the performance of machine learning models. Popular independent ensembles (IE) relying on naive averaging/voting scheme have been of typical choice for most applications involving deep neural networks, but they do not consider advanced collaboration among ensemble models. In this paper, we propose new ensemble methods specialized for deep neural networks, called confident multiple choice learning (CMCL): it is a variant of multiple choice learning (MCL) via addressing its overconfidence this http URL particular, the proposed major components of CMCL beyond the original MCL scheme are (i) new loss, i.e., confident oracle loss, (ii) new architecture, i.e., feature sharing and (iii) new training method, i.e., stochastic labeling. We demonstrate the effect of CMCL via experiments on the image classification on CIFAR and SVHN, and the foreground-background segmentation on the iCoseg. In particular, CMCL using 5 residual networks provides 14.05% and 6.60% relative reductions in the top-1 error rates from the corresponding IE scheme for the classification task on CIFAR and SVHN, respectively.

Connected through hyperedge HE-1 (induced by source paper "end to end content and plan selection for data to text generation"):
  with members:
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "unsupervised natural language generation with denoising autoencoders"
    - "findings of the e2e nlg challenge"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "get to the point summarization with pointer generator networks"
    - "addressing the data sparsity issue in neural amr parsing"

Connected through hyperedge HE-2 (induced by source paper "anomaly detection with multiple hypotheses predictions"):
  with members:
    - "novelty detection with gan"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "photographic image synthesis with cascaded refinement networks"
    - "learning in an uncertain world representing ambiguity through multiple hypotheses"
    - "stochastic multiple choice learning for training diverse deep ensembles"

Connected through hyperedge HE-3 (induced by source paper "learning in an uncertain world representing ambiguity through multiple hypotheses"):
  with members:
    - "deep label distribution learning with label ambiguity"
    - "hcp a flexible cnn framework for multi label image classification"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "deeper depth prediction with fully convolutional residual networks"
    - "cnn rnn a unified framework for multi label image classification"
    - "learning physical intuition of block towers by example"
    - "an uncertain future forecasting from static images using variational autoencoders"

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)