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 pivots for image caption translation"
  Abstract: We present an approach to improve statistical machine translation of image descriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over a database of images that are captioned in the target language, and use the captions of the most similar images for crosslingual reranking of translation outputs. Our approach does not depend on the availability of large amounts of in-domain parallel data, but only relies on available large datasets of monolingually captioned images, and on state-of-the-art convolutional neural networks to compute image similarities. Our experimental evaluation shows improvements of 1 BLEU point over strong baselines.

Connected through hyperedge HE-1 (induced by source paper "mule multimodal universal language embedding"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "vico word embeddings from visual co occurrences"
    - "language features matter effective language representations for vision language tasks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "stacked cross attention for image text matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unpaired image captioning by language pivoting"
    - "multi task learning of hierarchical vision language representation"

Connected through hyperedge HE-2 (induced by source paper "unpaired image captioning via scene graph alignments"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "engaging image captioning via personality"

Connected through hyperedge HE-3 (induced by source paper "unpaired cross lingual image caption generation with self supervised rewards"):
  with members:
    - "unsupervised bilingual lexicon induction from mono lingual multimodal data"
    - "good news everyone context driven entity aware captioning for news images"
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "discriminability objective for training descriptive captions"
    - "context aware visual policy network for sequence level image captioning"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"

Connected through hyperedge HE-4 (induced by source paper "countering language drift via visual grounding"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "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"
    - "natural language does not emerge naturally in multi agent dialog"
    - "towards neural machine translation with partially aligned corpora"

Connected through hyperedge HE-5 (induced by source paper "aligning multilingual word embeddings for cross modal retrieval task"):
  with members:
    - "neural baby talk"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "learning two branch neural networks for image text matching tasks"
    - "multilingual multi modal embeddings for natural language processing"
    - "image pivoting for learning multilingual multimodal representations"
    - "stair captions constructing a large scale japanese image caption dataset"
    - "word translation without parallel data"
    - "instance aware image and sentence matching with selective multimodal lstm"

Connected through hyperedge HE-6 (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"
    - "zero resource neural machine translation with multi agent communication game"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "cross lingual visual verb sense disambiguation"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "sockeye a toolkit for neural machine translation"
    - "cuni system for the wmt17 multimodal translation task"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "image pivoting for learning multilingual multimodal representations"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-8 (induced by source paper "unpaired image captioning by language pivoting"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "zero resource neural machine translation with multi agent communication game"
    - "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"

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)