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: "multi30k multilingual english german image descriptions"
  Abstract: We introduce the Multi30K dataset to stimulate multilingual multimodal research. Recent advances in image description have been demonstrated on English-language datasets almost exclusively, but image description should not be limited to English. This dataset extends the Flickr30K dataset with i) German translations created by professional translators over a subset of the English descriptions, and ii) descriptions crowdsourced independently of the original English descriptions. We outline how the data can be used for multilingual image description and multimodal machine translation, but we anticipate the data will be useful for a broader range of tasks.

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"
    - "unsupervised multi modal neural machine translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "investigating the decoders of maximum likelihood sequence models a look ahead approach"):
  with members:
    - "on nmt search errors and model errors cat got your tongue"
    - "end to end dense video captioning with masked transformer"
    - "a call for clarity in reporting bleu scores"
    - "tvqa localized compositional video question answering"
    - "optimal completion distillation for sequence learning"
    - "attention is all you need"
    - "adversarial learning for neural dialogue generation"
    - "towards better decoding and language model integration in sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "low complexity lstm training and inference with floatsd8 weight representation"):
  with members:
    - "qpytorch a low precision arithmetic simulation framework"
    - "pact parameterized clipping activation for quantized neural networks"
    - "mixed precision training"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "pointer sentinel mixture models"
    - "fixed point quantization of deep convolutional networks"
    - "learning both weights and connections for efficient neural networks"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "a simple and robust convolutional attention network for irregular text recognition"):
  with members:
    - "universal transformers"
    - "single shot scene text retrieval"
    - "scene text recognition from two dimensional perspective"
    - "nrtr a no recurrence sequence to sequence model for scene text recognition"
    - "edit probability for scene text recognition"
    - "scan sliding convolutional attention network for scene text recognition"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "show attend and read a simple and strong baseline for irregular text recognition"

Connected through hyperedge HE-7 (induced by source paper "image search using multilingual texts a cross modal learning approach between image and text"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "vse improving visual semantic embeddings with hard negatives"
    - "word translation without parallel data"
    - "end to end learning of deep visual representations for image retrieval"
    - "aggregated residual transformations for deep neural networks"
    - "fully convolutional siamese networks for object tracking"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "cost sensitive training for autoregressive models"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "on the weaknesses of reinforcement learning for neural machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "non monotonic sequential text generation"
    - "beyond bleu training neural machine translation with semantic similarity"
    - "on nmt search errors and model errors cat got your tongue"
    - "optimal completion distillation for sequence learning"
    - "loss functions for multiset prediction"

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)