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 cipher cracking using discrete gans"
  Abstract: This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data enciphered using shift and Vigenere ciphers to a high degree of fidelity and for vocabularies much larger than previously achieved. We present how CycleGAN can be made compatible with discrete data and train in a stable way. We then prove that the technique used in CipherGAN avoids the common problem of uninformative discrimination associated with GANs applied to discrete data.

Connected through hyperedge HE-1 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

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"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "unsupervised machine translation using monolingual corpora only"
    - "improved variational autoencoders for text modeling using dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "adversarial text generation via feature mover s distance"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "improving gans using optimal transport"
    - "jointgan multi domain joint distribution learning with generative adversarial nets"
    - "texygen a benchmarking platform for text generation models"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "learning to read by spelling towards unsupervised text recognition"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised sequence classification using sequential output statistics"
    - "unsupervised neural machine translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"):
  with members:
    - "neural style transfer for audio spectograms"
    - "modulated variational auto encoders for many to many musical timbre transfer"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "music style transfer issues a position paper"
    - "a universal music translation network"
    - "symbolic music genre transfer with cyclegan"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "meansum a neural model for unsupervised multi document abstractive summarization"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "toward abstractive summarization using semantic representations"
    - "learning to encode text as human readable summaries using generative adversarial networks"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "state of the art speech recognition with sequence to sequence models"
    - "unsupervised neural machine translation"
    - "get to the point summarization with pointer generator networks"

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)