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: "supervising neural attention models for video captioning by human gaze data"
  Abstract: The attention mechanisms in deep neural networks are inspired by human's attention that sequentially focuses on the most relevant parts of the information over time to generate prediction output. The attention parameters in those models are implicitly trained in an end-to-end manner, yet there have been few trials to explicitly incorporate human gaze tracking to supervise the attention models. In this paper, we investigate whether attention models can benefit from explicit human gaze labels, especially for the task of video captioning. We collect a new dataset called VAS, consisting of movie clips, and corresponding multiple descriptive sentences along with human gaze tracking data. We propose a video captioning model named Gaze Encoding Attention Network (GEAN) that can leverage gaze tracking information to provide the spatial and temporal attention for sentence generation. Through evaluation of language similarity metrics and human assessment via Amazon mechanical Turk, we demonstrate that spatial attentions guided by human gaze data indeed improve the performance of multiple captioning methods. Moreover, we show that the proposed approach achieves the state-of-the-art performance for both gaze prediction and video captioning not only in our VAS dataset but also in standard datasets (e.g. LSMDC and Hollywood2).

Connected through hyperedge HE-1 (induced by source paper "attention flow end to end joint attention estimation"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "teacher s perception in the classroom"
    - "cbam convolutional block attention module"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "residual attention network for image classification"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-2 (induced by source paper "tvqa spatio temporal grounding for video question answering"):
  with members:
    - "neural baby talk"
    - "know what you don t know unanswerable questions for squad"
    - "mattnet modular attention network for referring expression comprehension"
    - "grounded video description"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-3 (induced by source paper "deep ordinal hashing with spatial attention"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "end to end comparative attention networks for person re identification"
    - "deep region hashing for efficient large scale instance search from images"
    - "fooling vision and language models despite localization and attention mechanism"
    - "image captioning with semantic attention"
    - "weakly supervised cascaded convolutional networks"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "attention based glaucoma detection a large scale database and cnn model"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "grounded video description"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "move forward and tell a progressive generator of video descriptions"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"

Connected through hyperedge HE-6 (induced by source paper "less is more picking informative frames for video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "reconstruction network for video captioning"
    - "weakly supervised dense video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "to click or not to click automatic selection of beautiful thumbnails from videos"
    - "image captioning with semantic attention"
    - "hierarchical co attention for visual question answering"
    - "frame and segment level features and candidate pool evaluation for video caption generation"

Connected through hyperedge HE-7 (induced by source paper "semantic aware attention based deep object co segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "deep object co segmentation"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "object cosegmentation using deep siamese network"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "squeeze and excitation networks"
    - "structured attentions for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "adversarial tableqa attention supervision for question answering on tables"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "question answering through transfer learning from large fine grained supervision data"
    - "stochastic answer networks for machine reading comprehension"
    - "weakly supervised semantic parsing with abstract examples"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "exploring human like attention supervision in visual question answering"
    - "adversarial examples for evaluating reading comprehension systems"
    - "language to logical form with neural attention"

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)