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: "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"
  Abstract: We propose a model to learn visually grounded word embeddings (vis-w2v) to capture visual notions of semantic relatedness. While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world. For instance, although "eats" and "stares at" seem unrelated in text, they share semantics visually. When people are eating something, they also tend to stare at the food. Grounding diverse relations like "eats" and "stares at" into vision remains challenging, despite recent progress in vision. We note that the visual grounding of words depends on semantics, and not the literal pixels. We thus use abstract scenes created from clipart to provide the visual grounding. We find that the embeddings we learn capture fine-grained, visually grounded notions of semantic relatedness. We show improvements over text-only word embeddings (word2vec) on three tasks: common-sense assertion classification, visual paraphrasing and text-based image retrieval. Our code and datasets are available online.

Connected through hyperedge HE-1 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "deep contextualized word representations"
    - "an efficient framework for learning sentence representations"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "learning visually grounded sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "personalizing fast forward videos based on visual and textual features from social network"):
  with members:
    - "making a long story short a multi importance fast forwarding egocentric videos with the emphasis on relevant objects"
    - "a weighted sparse sampling and smoothing frame transition approach for semantic fast forward first person videos"
    - "ffnet video fast forwarding via reinforcement learning"
    - "video salient object detection via fully convolutional networks"
    - "egosampling wide view hyperlapse from egocentric videos"
    - "predicting visual features from text for image and video caption retrieval"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "query focused video summarization dataset evaluation and a memory network based approach"

Connected through hyperedge HE-4 (induced by source paper "multimodal representation model based on graph based rank fusion"):
  with members:
    - "fusion vectors embedding graph fusions for efficient unsupervised rank aggregation"
    - "unsupervised graph based rank aggregation for improved retrieval"
    - "do better imagenet models transfer better"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "learning transferable architectures for scalable image recognition"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "wasserstein barycenter model ensembling"):
  with members:
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "improving gans using optimal transport"
    - "adversarial semantic alignment for improved image captions"
    - "approximating the quadratic transportation metric in near linear time"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "zero shot learning the good the bad and the ugly"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-6 (induced by source paper "composing and embedding the words as classifiers model of grounded semantics"):
  with members:
    - "linguistic generalization and compositionality in modern artificial neural networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "semantic composition via probabilistic model theory"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "going deeper with convolutions"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-7 (induced by source paper "word2vec to behavior morphology facilitates the grounding of language in machines"):
  with members:
    - "automated shapeshifting for function recovery in damaged robots"
    - "learning agile and dynamic motor skills for legged robots"
    - "grasp2vec learning object representations from self supervised grasping"
    - "sound word2vec learning word representations grounded in sounds"
    - "morphology dictates a robot s ability to ground crowd proposed language"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "learning from noisy web data with category level supervision"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "zero shot learning via category specific visual semantic mapping"
    - "attend in groups a weakly supervised deep learning framework for learning from web data"
    - "places an image database for deep scene understanding"
    - "few shot object recognition from machine labeled web images"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "webly supervised learning of convolutional networks"

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)