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: "multimodal attention for neural machine translation"
  Abstract: The attention mechanism is an important part of the neural machine translation (NMT) where it was reported to produce richer source representation compared to fixed-length encoding sequence-to-sequence models. Recently, the effectiveness of attention has also been explored in the context of image captioning. In this work, we assess the feasibility of a multimodal attention mechanism that simultaneously focus over an image and its natural language description for generating a description in another language. We train several variants of our proposed attention mechanism on the Multi30k multilingual image captioning dataset. We show that a dedicated attention for each modality achieves up to 1.6 points in BLEU and METEOR compared to a textual NMT baseline.

Connected through hyperedge HE-1 (induced by source paper "multimodal data fusion based on the global workspace theory"):
  with members:
    - "automatic detection of protective behavior in chronic pain physical rehabilitation a recurrent neural network approach"
    - "multi level multimodal common semantic space for image phrase grounding"
    - "an introductory survey on attention mechanisms in nlp problems"
    - "multimodal machine learning a survey and taxonomy"
    - "attention is all you need"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "video description a survey of methods datasets and evaluation metrics"
    - "nocaps novel object captioning at scale"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"

Connected through hyperedge HE-3 (induced by source paper "probing the need for visual context in multimodal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "attention strategies for multi source sequence to sequence learning"
    - "osu multimodal machine translation system report"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-4 (induced by source paper "on leveraging the visual modality for neural machine translation"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "improving sequence to sequence learning via optimal transport"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "how2 a large scale dataset for multimodal language understanding"
    - "imagination improves multimodal translation"
    - "attention strategies for multi source sequence to sequence learning"
    - "all but the top simple and effective postprocessing for word representations"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "apply chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "improving character level japanese chinese neural machine translation with radicals as an additional input feature"
    - "imagination improves multimodal translation"
    - "word subword or character an empirical study of granularity in chinese english nmt"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "multilingual multi modal embeddings for natural language processing"
    - "towards string to tree neural machine translation"
    - "learning character level compositionality with visual features"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "emergent translation in multi agent communication"):
  with members:
    - "translating neuralese"
    - "imagination improves multimodal translation"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "learning visually grounded sentence representations"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "crossmodal attentive skill learner"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "when waiting is not an option learning options with a deliberation cost"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "the option critic architecture"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "reinforcement learning through asynchronous advantage actor critic on a gpu"
    - "modular multitask reinforcement learning with policy sketches"

Connected through hyperedge HE-8 (induced by source paper "nmtpy a flexible toolkit for advanced neural machine translation systems"):
  with members:
    - "lium machine translation systems for wmt17 news translation task"
    - "opennmt open source toolkit for neural machine translation"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "using the output embedding to improve language models"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "neural machine translation of rare words with subword units"
    - "multilingual image description with neural sequence models"

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)