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: "multilingual multi modal embeddings for natural language processing"
  Abstract: We propose a novel discriminative model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce a modification of a pairwise contrastive estimation optimisation function as our training objective. We evaluate our embeddings on an image-sentence ranking (ISR), a semantic textual similarity (STS), and a neural machine translation (NMT) task. We find that the additional multilingual signals lead to improvements on both the ISR and STS tasks, and the discriminative cost can also be used in re-ranking $n$-best lists produced by NMT models, yielding strong improvements.

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 "multi head attention with diversity for learning grounded multilingual multimodal representations"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "imagination improves multimodal translation"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "multimodal pivots for image caption translation"

Connected through hyperedge HE-4 (induced by source paper "learning multilingual word embeddings using image text data"):
  with members:
    - "graph rise graph regularized image semantic embedding"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "image pivoting for learning multilingual multimodal representations"
    - "word translation without parallel data"
    - "massively multilingual word embeddings"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "lessons learned in multilingual grounded language learning"):
  with members:
    - "vision as an interlingua learning multilingual semantic embeddings of untranscribed speech"
    - "imagination improves multimodal translation"
    - "learning visually grounded sentence representations"
    - "findings of the second shared task on multimodal machine translation and multilingual image description"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning language representations for typology prediction"
    - "attention strategies for multi source sequence to sequence learning"
    - "massively multilingual neural grapheme to phoneme conversion"

Connected through hyperedge HE-6 (induced by source paper "apply chinese radicals into neural machine translation deeper than character level"):
  with members:
    - "improving character level japanese chinese neural machine translation with radicals as an additional input feature"
    - "imagination improves multimodal translation"
    - "word subword or character an empirical study of granularity in chinese english nmt"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "towards string to tree neural machine translation"
    - "learning character level compositionality with visual features"
    - "six challenges for neural machine translation"
    - "context gates for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "a visual attention grounding neural model for multimodal machine translation"):
  with members:
    - "imagination improves multimodal translation"
    - "cuni system for the wmt17 multimodal translation task"
    - "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-8 (induced by source paper "emergent translation in multi agent communication"):
  with members:
    - "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"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

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)