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 and sequence to sequence models a tutorial"
  Abstract: This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.

Connected through hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-2 (induced by source paper "multi issue bargaining with deep reinforcement learning"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "strategic dialogue management via deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep learning based vehicle behaviour prediction for autonomous driving applications a review"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "grip graph based interaction aware trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "anomaly detection in road traffic using visual surveillance a survey"
    - "survey on vision based path prediction"

Connected through hyperedge HE-4 (induced by source paper "deep gamblers learning to abstain with portfolio theory"):
  with members:
    - "one shot federated learning"
    - "selectivenet a deep neural network with an integrated reject option"
    - "on the convergence of adam and beyond"
    - "multimodal language analysis with recurrent multistage fusion"
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

Connected through hyperedge HE-5 (induced by source paper "attention forcing for sequence to sequence model training"):
  with members:
    - "close to human quality tts with transformer"
    - "efficient neural audio synthesis"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "neural speech synthesis with transformer network"
    - "tacotron towards end to end speech synthesis"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "samplernn an unconditional end to end neural audio generation model"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-6 (induced by source paper "neural attention based deep learning architectures for modeling traffic dynamics on lane graphs"):
  with members:
    - "traffic queue length and pressure estimation for road networks with geometric deep learning algorithms"
    - "relational inductive biases deep learning and graph networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-7 (induced by source paper "on compositionality in neural machine translation"):
  with members:
    - "the compositionality of neural networks integrating symbolism and connectionism"
    - "jump to better conclusions scan both left and right"
    - "still not systematic after all these years on the compositional skills of sequence to sequence recurrent networks"
    - "attention is all you need"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "effective approaches to attention based neural machine translation"
    - "sequence to sequence video to text"
    - "a neural conversational model"

Connected through hyperedge HE-8 (induced by source paper "towards neural decompilation"):
  with members:
    - "tree to tree neural networks for program translation"
    - "codesum translate program language to natural language"
    - "neural attribute machines for program generation"
    - "six challenges for neural machine translation"
    - "a neural architecture for generating natural language descriptions from source code changes"
    - "a convolutional attention network for extreme summarization of source code"
    - "tensorflow a system for large scale machine learning"
    - "on the properties of neural machine translation encoder decoder approaches"

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)