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: "improving neural machine translation with conditional sequence generative adversarial nets"
  Abstract: This paper proposes an approach for applying GANs to NMT. We build a conditional sequence generative adversarial net which comprises of two adversarial sub models, a generator and a discriminator. The generator aims to generate sentences which are hard to be discriminated from human-translated sentences (i.e., the golden target sentences), And the discriminator makes efforts to discriminate the machine-generated sentences from human-translated ones. The two sub models play a mini-max game and achieve the win-win situation when they reach a Nash Equilibrium. Additionally, the static sentence-level BLEU is utilized as the reinforced objective for the generator, which biases the generation towards high BLEU points. During training, both the dynamic discriminator and the static BLEU objective are employed to evaluate the generated sentences and feedback the evaluations to guide the learning of the generator. Experimental results show that the proposed model consistently outperforms the traditional RNNSearch and the newly emerged state-of-the-art Transformer on English-German and Chinese-English translation tasks.

Connected through hyperedge HE-1 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "on convergence and stability of gans"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic neural machine translation using amr"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"

Connected through hyperedge HE-4 (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-5 (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"
    - "adversarial neural machine translation"

Connected through hyperedge HE-6 (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"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "adversarially regularized autoencoders"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"

Connected through hyperedge HE-7 (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-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)