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: "neural machine translation with gumbel greedy decoding"
  Abstract: Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time. In this paper, we propose the Gumbel-Greedy Decoding which trains a generative network to predict translation under a trained model. We solve such a problem using the Gumbel-Softmax reparameterization, which makes our generative network differentiable and trainable through standard stochastic gradient methods. We empirically demonstrate that our proposed model is effective for generating sequences of discrete words.

Connected through hyperedge HE-1 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "direct policy gradients direct optimization of policies in discrete action spaces"
    - "direct optimization through arg max for discrete variational auto encoder"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "classical structured prediction losses for sequence to sequence learning"
    - "searnn training rnns with global local losses"

Connected through hyperedge HE-2 (induced by source paper "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-3 (induced by source paper "catgan category aware generative adversarial networks with hierarchical evolutionary learning for category text generation"):
  with members:
    - "on accurate evaluation of gans for language generation"
    - "the relativistic discriminator a key element missing from standard gan"
    - "adversarial text generation via feature mover s distance"
    - "evolutionary generative adversarial networks"
    - "relational recurrent neural networks"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"

Connected through hyperedge HE-4 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "listening while speaking speech chain by deep learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial reprogramming of text classification neural networks"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "adversarial patch"
    - "adversarial reprogramming of neural networks"
    - "greedy attack and gumbel attack generating adversarial examples for discrete data"
    - "black box attacks against rnn based malware detection algorithms"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-6 (induced by source paper "a continuous relaxation of beam search for end to end training of neural sequence models"):
  with members:
    - "neural machine translation and sequence to sequence models a tutorial"
    - "differentiable scheduled sampling for credit assignment"
    - "six challenges for neural machine translation"
    - "sequence to sequence learning as beam search optimization"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "an actor critic algorithm for sequence prediction"
    - "the concrete distribution a continuous relaxation of discrete random variables"

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)