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: "context gates for neural machine translation"
  Abstract: In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a translation while target contexts affect the fluency. Intuitively, generation of a content word should rely more on the source context and generation of a functional word should rely more on the target context. Due to the lack of effective control over the influence from source and target contexts, conventional NMT tends to yield fluent but inadequate translations. To address this problem, we propose context gates which dynamically control the ratios at which source and target contexts contribute to the generation of target words. In this way, we can enhance both the adequacy and fluency of NMT with more careful control of the information flow from contexts. Experiments show that our approach significantly improves upon a standard attention-based NMT system by +2.3 BLEU points.

Connected through hyperedge HE-1 (induced by source paper "show edit and tell a framework for editing image captions"):
  with members:
    - "attention on attention for image captioning"
    - "look and modify modification networks for image captioning"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-2 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-3 (induced by source paper "interrogating the explanatory power of attention in neural machine translation"):
  with members:
    - "saliency driven word alignment interpretation for neural machine translation"
    - "model explanations under calibration"
    - "an analysis of attention over clinical notes for predictive tasks"
    - "analyzing the structure of attention in a transformer language model"
    - "attention is not explanation"
    - "do transformer attention heads provide transparency in abstractive summarization"
    - "what does bert look at an analysis of bert s attention"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"

Connected through hyperedge HE-4 (induced by source paper "modeling recurrence for transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "context aware neural machine translation learns anaphora resolution"

Connected through hyperedge HE-5 (induced by source paper "bidirectional context aware hierarchical attention network for document understanding"):
  with members:
    - "dialogue act classification with context aware self attention"
    - "energy based self attentive learning of abstractive communities for spoken language understanding"
    - "context aware self attention networks"
    - "deep communicating agents for abstractive summarization"
    - "improving the transformer translation model with document level context"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "context aware neural machine translation learns anaphora resolution"
    - "three factors influencing minima in sgd"

Connected through hyperedge HE-6 (induced by source paper "document level neural machine translation with inter sentence attention"):
  with members:
    - "semantic role labeling with associated memory network"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "extreme adaptation for personalized neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"

Connected through hyperedge HE-7 (induced by source paper "learning to select track and generate for data to text"):
  with members:
    - "on the convergence of adam and beyond"
    - "entity tracking improves cloze style reading comprehension"
    - "data to text generation with content selection and planning"
    - "challenges in data to document generation"
    - "dynamic entity representations in neural language models"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-8 (induced by source paper "ensuring readability and data fidelity using head modifier templates in deep type description generation"):
  with members:
    - "generating fine grained open vocabulary entity type descriptions"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "fine grained entity type classification by jointly learning representations and label embeddings"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "pointing the unknown words"

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)