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: "text guided attention model for image captioning"
  Abstract: Visual attention plays an important role to understand images and demonstrates its effectiveness in generating natural language descriptions of images. On the other hand, recent studies show that language associated with an image can steer visual attention in the scene during our cognitive process. Inspired by this, we introduce a text-guided attention model for image captioning, which learns to drive visual attention using associated captions. For this model, we propose an exemplar-based learning approach that retrieves from training data associated captions with each image, and use them to learn attention on visual features. Our attention model enables to describe a detailed state of scenes by distinguishing small or confusable objects effectively. We validate our model on MS-COCO Captioning benchmark and achieve the state-of-the-art performance in standard metrics.

Connected through hyperedge HE-1 (induced by source paper "streamlined dense video captioning"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "move forward and tell a progressive generator of video descriptions"
    - "end to end dense video captioning with masked transformer"
    - "jointly localizing and describing events for dense video captioning"
    - "reconstruction network for video captioning"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "attention is all you need"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-2 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-3 (induced by source paper "cascaded revision network for novel object captioning"):
  with members:
    - "neural baby talk"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "decoupled novel object captioner"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "guided open vocabulary image captioning with constrained beam search"

Connected through hyperedge HE-4 (induced by source paper "learning good representation via continuous attention"):
  with members:
    - "learn to pay attention"
    - "attention is all you need"
    - "curiosity driven exploration by self supervised prediction"
    - "image captioning with semantic attention"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"

Connected through hyperedge HE-5 (induced by source paper "recurrent fusion network for image captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "learning to guide decoding for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "reconstruction network for video captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-6 (induced by source paper "bringing back simplicity and lightliness into neural image captioning"):
  with members:
    - "umons submission for wmt18 multimodal translation task"
    - "improved image captioning via policy gradient optimization of spider"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "attention is all you need"
    - "visually grounded word embeddings and richer visual features for improving multimodal neural machine translation"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an empirical study on the effectiveness of images in multimodal neural machine translation"
    - "lium cvc submissions for wmt17 multimodal translation task"

Connected through hyperedge HE-7 (induced by source paper "attentive semantic alignment with offset aware correlation kernels"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "large scale image retrieval with attentive deep local features"
    - "learning dense correspondence via 3d guided cycle consistency"

Connected through hyperedge HE-8 (induced by source paper "regularizing rnns for caption generation by reconstructing the past with the present"):
  with members:
    - "learning to guide decoding for image captioning"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spice semantic propositional image caption evaluation"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

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)