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: "unsupervised multi modal neural machine translation"
  Abstract: Unsupervised neural machine translation (UNMT) has recently achieved remarkable results with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performance of UNMT. Our assumption is intuitively based on the invariant property of image, i.e., the description of the same visual content by different languages should be approximately similar. We propose an unsupervised multi-modal machine translation (UMNMT) framework based on the language translation cycle consistency loss conditional on the image, targeting to learn the bidirectional multi-modal translation simultaneously. Through an alternate training between multi-modal and uni-modal, our inference model can translate with or without the image. On the widely used Multi30K dataset, the experimental results of our approach are significantly better than those of the text-only UNMT on the 2016 test dataset.

Connected through hyperedge HE-1 (induced by source paper "visual grounding in video for unsupervised word translation"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning to follow directions in street view"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "learning visual question answering by bootstrapping hard attention"
    - "lifelong learning for image captioning by asking natural language questions"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "lattice transformer for speech translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "chinese ner using lattice lstm"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "bilingual expert can find translation errors"
    - "self attention with relative position representations"
    - "neural lattice language models"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "visual agreement regularized training for multi modal machine translation"):
  with members:
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "distilling translations with visual awareness"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "latent variable model for multi modal translation"
    - "phrase based neural unsupervised machine translation"
    - "imagination improves multimodal translation"

Connected through hyperedge HE-4 (induced by source paper "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"):
  with members:
    - "zero resource neural machine translation with multi agent communication game"
    - "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"

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.CV(Computer Vision and Pattern Recognition)