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: "zero resource neural machine translation with multi agent communication game"
  Abstract: While end-to-end neural machine translation (NMT) has achieved notable success in the past years in translating a handful of resource-rich language pairs, it still suffers from the data scarcity problem for low-resource language pairs and domains. To tackle this problem, we propose an interactive multimodal framework for zero-resource neural machine translation. Instead of being passively exposed to large amounts of parallel corpora, our learners (implemented as encoder-decoder architecture) engage in cooperative image description games, and thus develop their own image captioning or neural machine translation model from the need to communicate in order to succeed at the game. Experimental results on the IAPR-TC12 and Multi30K datasets show that the proposed learning mechanism significantly improves over the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "improved zero shot neural machine translation via ignoring spurious correlations"):
  with members:
    - "cross lingual language model pretraining"
    - "the missing ingredient in zero shot neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "zero shot dual machine translation"
    - "a teacher student framework for zero resource neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping disjoint datasets for multilingual multimodal representation learning"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "lessons learned in multilingual grounded language learning"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "limitations of cross lingual learning from image search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "emergent translation in multi agent communication"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-3 (induced by source paper "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"):
  with members:
    - "unsupervised multi modal neural machine translation"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "phrase based neural unsupervised machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised neural machine translation"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "multi30k multilingual english german image descriptions"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "unpaired image captioning by language pivoting"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "texygen a benchmarking platform for text generation models"
    - "improved image captioning via policy gradient optimization of spider"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "mat a multimodal attentive translator for image captioning"

Connected through hyperedge HE-5 (induced by source paper "meta learning for low resource neural machine translation"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "unsupervised neural machine translation with weight sharing"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "unsupervised neural machine translation"
    - "emergent translation in multi agent communication"

Connected through hyperedge HE-6 (induced by source paper "unsupervised multi modal neural machine translation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "unsupervised neural machine translation"
    - "inversenet solving inverse problems with splitting networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-7 (induced by source paper "exploration on grounded word embedding matching words and images with image enhanced skip gram model"):
  with members:
    - "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"
    - "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"
    - "label embedding network learning label representation for soft training of deep networks"
    - "query and output generating words by querying distributed word representations for paraphrase generation"
    - "image pivoting for learning multilingual multimodal representations"
    - "exploring models and data for image question answering"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"

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)