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: "adversarial neural machine translation"
  Abstract: In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success of generative adversarial networks (GANs), we employ an adversarial training architecture and name it as Adversarial-NMT. In Adversarial-NMT, the training of the NMT model is assisted by an adversary, which is an elaborately designed Convolutional Neural Network (CNN). The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human. The goal of the NMT model is to produce high quality translations so as to cheat the adversary. A policy gradient method is leveraged to co-train the NMT model and the adversary. Experimental results on English$\rightarrow$French and German$\rightarrow$English translation tasks show that Adversarial-NMT can achieve significantly better translation quality than several strong baselines.

Connected through hyperedge HE-1 (induced by source paper "vibe video inference for human body pose and shape estimation"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "self supervised learning of 3d human pose using multi view geometry"
    - "structured prediction helps 3d human motion modelling"
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "from variational to deterministic autoencoders"
    - "expressive body capture 3d hands face and body from a single image"

Connected through hyperedge HE-2 (induced by source paper "on the weaknesses of reinforcement learning for neural machine translation"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "a study of reinforcement learning for neural machine translation"
    - "diversity is all you need learning skills without a reward function"
    - "evaluating text gans as language models"
    - "language gans falling short"
    - "thumt an open source toolkit for neural machine translation"
    - "parameter space noise for exploration"

Connected through hyperedge HE-3 (induced by source paper "latent code and text based generative adversarial networks for soft text generation"):
  with members:
    - "bilingual gan a step towards parallel text generation"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "spectral normalization for generative adversarial networks"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "generating continuous representations of medical texts"
    - "toward controlled generation of text"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "bilingual gan a step towards parallel text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "on accurate evaluation of gans for language generation"
    - "phrase based neural unsupervised machine translation"
    - "generating continuous representations of medical texts"
    - "attention is all you need"
    - "adversarially regularized autoencoders"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "retrieving sequential information for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "you may not need attention"
    - "a study of reinforcement learning for neural machine translation"
    - "fast decoding in sequence models using discrete latent variables"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bag of words as target for neural machine translation"
    - "speeding up neural machine translation decoding by cube pruning"

Connected through hyperedge HE-6 (induced by source paper "quantifying exposure bias for neural language generation"):
  with members:
    - "bridging the gap between training and inference for neural machine translation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "on accurate evaluation of gans for language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"
    - "language gans falling short"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-7 (induced by source paper "negative training for neural dialogue response generation"):
  with members:
    - "deep text classification can be fooled"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "detecting egregious responses in neural sequence to sequence models"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "towards crafting text adversarial samples"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "adversarial learning for neural dialogue generation"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "train sort explain learning to diagnose translation models"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "innvestigate neural networks"
    - "learning explanations from language data"
    - "methods for interpreting and understanding deep neural networks"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "explaining recurrent neural network predictions in sentiment analysis"

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.CL(Computation and Language)