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: "addressing the rare word problem in neural machine translation"
  Abstract: Neural Machine Translation (NMT) is a new approach to machine translation that has shown promising results that are comparable to traditional approaches. A significant weakness in conventional NMT systems is their inability to correctly translate very rare words: end-to-end NMTs tend to have relatively small vocabularies with a single unk symbol that represents every possible out-of-vocabulary (OOV) word. In this paper, we propose and implement an effective technique to address this problem. We train an NMT system on data that is augmented by the output of a word alignment algorithm, allowing the NMT system to emit, for each OOV word in the target sentence, the position of its corresponding word in the source sentence. This information is later utilized in a post-processing step that translates every OOV word using a dictionary. Our experiments on the WMT14 English to French translation task show that this method provides a substantial improvement of up to 2.8 BLEU points over an equivalent NMT system that does not use this technique. With 37.5 BLEU points, our NMT system is the first to surpass the best result achieved on a WMT14 contest task.

Connected through hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "the evolved transformer"

Connected through hyperedge HE-2 (induced by source paper "multi layer representation fusion for neural machine translation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "towards neural phrase based machine translation"
    - "attention is all you need"
    - "stronger baselines for trustable results in neural machine translation"
    - "classical structured prediction losses for sequence to sequence learning"
    - "multi channel encoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-3 (induced by source paper "differentiable top k operator with optimal transport"):
  with members:
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "reparameterizable subset sampling via continuous relaxations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "fine grained video categorization with redundancy reduction attention"
    - "neural nearest neighbors networks"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "optnet differentiable optimization as a layer in neural networks"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"

Connected through hyperedge HE-4 (induced by source paper "urdu english machine transliteration using neural networks"):
  with members:
    - "how much does tokenization affect neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "six challenges for neural machine translation"
    - "natural language processing state of the art current trends and challenges"
    - "neural machine transliteration preliminary results"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "generating black box adversarial examples for text classifiers using a deep reinforced model"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "gray box adversarial training"
    - "adversarial texts with gradient methods"
    - "motivating the rules of the game for adversarial example research"

Connected through hyperedge HE-6 (induced by source paper "adversarial bootstrapping for dialogue model training"):
  with members:
    - "an adversarial learning framework for a persona based multi turn dialogue model"
    - "another diversity promoting objective function for neural dialogue generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "multi turn dialogue response generation in an adversarial learning framework"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "attention is all you need"
    - "hierarchical recurrent attention network for response generation"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "sequencer sequence to sequence learning for end to end program repair"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "an empirical study of fault localization families and their combinations"
    - "automatic software repair a bibliography"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to generate corrective patches using neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "understanding lstm a tutorial into long short term memory recurrent neural networks"):
  with members:
    - "listen attend and spell"
    - "grid long short term memory"
    - "attention based models for speech recognition"
    - "a neural conversational model"
    - "neural turing machines"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "a clockwork rnn"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)