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: "probing the need for visual context in multimodal machine translation"
  Abstract: Current work on multimodal machine translation (MMT) has suggested that the visual modality is either unnecessary or only marginally beneficial. We posit that this is a consequence of the very simple, short and repetitive sentences used in the only available dataset for the task (Multi30K), rendering the source text sufficient as context. In the general case, however, we believe that it is possible to combine visual and textual information in order to ground translations. In this paper we probe the contribution of the visual modality to state-of-the-art MMT models by conducting a systematic analysis where we partially deprive the models from source-side textual context. Our results show that under limited textual context, models are capable of leveraging the visual input to generate better translations. This contradicts the current belief that MMT models disregard the visual modality because of either the quality of the image features or the way they are integrated into the model.

Connected through hyperedge HE-1 (induced by source paper "don t take the easy way out ensemble based methods for avoiding known dataset biases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "compositional questions do not necessitate multi hop reasoning"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "understanding dataset design choices for multi hop reasoning"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "misleading failures of partial input baselines"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "robust machine comprehension models via adversarial training"

Connected through hyperedge HE-2 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research"):
  with members:
    - "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"
    - "reinforced video captioning with entailment rewards"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"

Connected through hyperedge HE-4 (induced by source paper "transformer based cascaded multimodal speech translation"):
  with members:
    - "predicting actions to help predict translations"
    - "distilling translations with visual awareness"
    - "achieving human parity on automatic chinese to english news translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-5 (induced by source paper "commonsense knowledge base completion with structural and semantic context"):
  with members:
    - "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"
    - "language models as knowledge bases"
    - "commonsense knowledge mining from pretrained models"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "answering science exam questions using query rewriting with background knowledge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "commonsense mining as knowledge base completion a study on the impact of novelty"
    - "end to end structure aware convolutional networks for knowledge base completion"

Connected through hyperedge HE-6 (induced by source paper "analyzing utility of visual context in multimodal speech recognition under noisy conditions"):
  with members:
    - "modality attention for end to end audio visual speech recognition"
    - "how2 a large scale dataset for multimodal language understanding"
    - "visual features for context aware speech recognition"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "attention strategies for multi source sequence to sequence learning"
    - "lipnet end to end sentence level lipreading"
    - "lip reading sentences in the wild"
    - "using the output embedding to improve language models"

Connected through hyperedge HE-7 (induced by source paper "on leveraging the visual modality for neural machine translation"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "how2 a large scale dataset for multimodal language understanding"
    - "imagination improves multimodal translation"
    - "attention strategies for multi source sequence to sequence learning"
    - "all but the top simple and effective postprocessing for word representations"
    - "multimodal attention for neural machine translation"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "predicting actions to help predict translations"):
  with members:
    - "distilling translations with visual awareness"
    - "tensor2tensor for neural machine translation"
    - "training tips for the transformer model"
    - "cuni system for the wmt18 multimodal translation task"
    - "how2 a large scale dataset for multimodal language understanding"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "multi30k multilingual english german image descriptions"

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)