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: "cuni system for the wmt17 multimodal translation task"
  Abstract: In this paper, we describe our submissions to the WMT17 Multimodal Translation Task. For Task 1 (multimodal translation), our best scoring system is a purely textual neural translation of the source image caption to the target language. The main feature of the system is the use of additional data that was acquired by selecting similar sentences from parallel corpora and by data synthesis with back-translation. For Task 2 (cross-lingual image captioning), our best submitted system generates an English caption which is then translated by the best system used in Task 1. We also present negative results, which are based on ideas that we believe have potential of making improvements, but did not prove to be useful in our particular setup.

Connected through hyperedge HE-1 (induced by source paper "probing representations learned by multimodal recurrent and transformer models"):
  with members:
    - "input combination strategies for multi source transformer decoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "cuni system for the wmt18 multimodal translation task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable textual neuron representations for nlp"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "imagination improves multimodal translation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"

Connected through hyperedge HE-3 (induced by source paper "a visual attention grounding neural model for multimodal machine translation"):
  with members:
    - "imagination improves multimodal translation"
    - "multilingual multi modal embeddings for natural language processing"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "image pivoting for learning multilingual multimodal representations"
    - "osu multimodal machine translation system report"
    - "neural machine translation of rare words with subword units"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "cuni system for the wmt18 multimodal translation task"):
  with members:
    - "attention is all you need"
    - "attention strategies for multi source sequence to sequence learning"
    - "cuni system for wmt16 automatic post editing and multimodal translation tasks"
    - "identity mappings in deep residual networks"
    - "using the output embedding to improve language models"
    - "improving neural machine translation models with monolingual data"
    - "show attend and tell neural image caption generation with visual attention"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence 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)