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: "training and evaluating multimodal word embeddings with large scale web annotated images"
  Abstract: In this paper, we focus on training and evaluating effective word embeddings with both text and visual information. More specifically, we introduce a large-scale dataset with 300 million sentences describing over 40 million images crawled and downloaded from publicly available Pins (i.e. an image with sentence descriptions uploaded by users) on Pinterest. This dataset is more than 200 times larger than MS COCO, the standard large-scale image dataset with sentence descriptions. In addition, we construct an evaluation dataset to directly assess the effectiveness of word embeddings in terms of finding semantically similar or related words and phrases. The word/phrase pairs in this evaluation dataset are collected from the click data with millions of users in an image search system, thus contain rich semantic relationships. Based on these datasets, we propose and compare several Recurrent Neural Networks (RNNs) based multimodal (text and image) models. Experiments show that our model benefits from incorporating the visual information into the word embeddings, and a weight sharing strategy is crucial for learning such multimodal embeddings. The project page is: this http URL

Connected through hyperedge HE-1 (induced by source paper "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"):
  with members:
    - "word learning and the acquisition of syntactic semantic overhypotheses"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "object level visual reasoning in videos"

Connected through hyperedge HE-2 (induced by source paper "harmonized multimodal learning with gaussian process latent variable models"):
  with members:
    - "multimodal machine learning a survey and taxonomy"
    - "deep multimodal representation learning from temporal data"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "variational auto encoded deep gaussian processes"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-3 (induced by source paper "learning visually grounded semantics from contrastive adversarial samples"):
  with members:
    - "natural language inference over interaction space"
    - "vse improving visual semantic embeddings with hard negatives"
    - "reasonet learning to stop reading in machine comprehension"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial examples for evaluating reading comprehension systems"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "dual attention networks for multimodal reasoning and matching"
    - "long short term memory networks for machine reading"

Connected through hyperedge HE-4 (induced by source paper "evaluating multimodal representations on sentence similarity vsts visual semantic textual similarity dataset"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "better text understanding through image to text transfer"
    - "learning robust visual semantic embeddings"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "teaching machines to describe images via natural language feedback"
    - "a decomposable attention model for natural language inference"
    - "deep residual learning for image recognition"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-5 (induced by source paper "demystifying core ranking in pinterest image search"):
  with members:
    - "neural ranking models with weak supervision"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "visual search at pinterest"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "better text understanding through image to text transfer"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning deep structure preserving image text embeddings"
    - "towards universal paraphrastic sentence embeddings"
    - "rethinking the inception architecture for computer vision"
    - "combining language and vision with a multimodal skip gram model"
    - "order embeddings of images and language"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "related pins at pinterest the evolution of a real world recommender system"):
  with members:
    - "visual discovery at pinterest"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "wide deep learning for recommender systems"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "human curation and convnets powering item to item recommendations on pinterest"
    - "visual search at pinterest"
    - "efficient estimation of word representations in vector space"

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.LG(Machine Learning)