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: "unsupervised learning of predictors from unpaired input output samples"
  Abstract: Unsupervised learning is the most challenging problem in machine learning and especially in deep learning. Among many scenarios, we study an unsupervised learning problem of high economic value --- learning to predict without costly pairing of input data and corresponding labels. Part of the difficulty in this problem is a lack of solid evaluation measures. In this paper, we take a practical approach to grounding unsupervised learning by using the same success criterion as for supervised learning in prediction tasks but we do not require the presence of paired input-output training data. In particular, we propose an objective function that aims to make the predicted outputs fit well the structure of the output while preserving the correlation between the input and the predicted output. We experiment with a synthetic structural prediction problem and show that even with simple linear classifiers, the objective function is already highly non-convex. We further demonstrate the nature of this non-convex optimization problem as well as potential solutions. In particular, we show that with regularization via a generative model, learning with the proposed unsupervised objective function converges to an optimal solution.

Connected through hyperedge HE-1 (induced by source paper "from caesar cipher to unsupervised learning a new method for classifier parameter estimation"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "learning to read by spelling towards unsupervised text recognition"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised attention guided image to image translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised text style transfer using language models as discriminators"):
  with members:
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "deep generative models with learnable knowledge constraints"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "unsupervised cipher cracking using discrete gans"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-3 (induced by source paper "unsupervised sequence classification using sequential output statistics"):
  with members:
    - "label free supervision of neural networks with physics and domain knowledge"
    - "semi supervised sequence learning"
    - "towards principled unsupervised learning"
    - "efficient estimation of word representations in vector space"
    - "sequence transduction with recurrent neural networks"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised pretraining for sequence to sequence learning"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "very deep convolutional networks for end to end speech recognition"
    - "zero resource translation with multi lingual neural machine translation"
    - "the edit distance transducer in action the university of cambridge english german system at wmt16"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "semi supervised learning for neural machine translation"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "exploring the limits of language modeling"

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)