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: "conceptlearner discovering visual concepts from weakly labeled image collections"
  Abstract: Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories. In this paper, we propose ConceptLearner, which is a scalable approach to discover visual concepts from weakly labeled image collections. Thousands of visual concept detectors are learned automatically, without human in the loop for additional annotation. We show that these learned detectors could be applied to recognize concepts at image-level and to detect concepts at image region-level accurately. Under domain-specific supervision, we further evaluate the learned concepts for scene recognition on SUN database and for object detection on Pascal VOC 2007. ConceptLearner shows promising performance compared to fully supervised and weakly supervised methods.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised segment annotation via expectation kernel density estimation"):
  with members:
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "feature and region selection for visual learning"
    - "weakly supervised deep detection networks"
    - "weakly supervised learning for salient object detection using background images"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning without prejudice avoiding bias in webly supervised action recognition"):
  with members:
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
    - "yfcc100m the new data in multimedia research"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "webly supervised learning of convolutional networks"
    - "a data driven approach for tag refinement and localization in web videos"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-3 (induced by source paper "complex event recognition from images with few training examples"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "eventnet a large scale structured concept library for complex event detection in video"
    - "automatic concept discovery from parallel text and visual corpora"
    - "selecting relevant web trained concepts for automated event retrieval"
    - "deep learning for single view instance recognition"
    - "building a large concept bank for representing events in video"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "image captioning with semantic attention"):
  with members:
    - "what value do explicit high level concepts have in vision to language problems"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "microsoft coco captions data collection and evaluation server"
    - "draw a recurrent neural network for image generation"
    - "simple image description generator via a linear phrase based approach"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "a semi supervised framework for image captioning"):
  with members:
    - "image captioning with semantic attention"
    - "encode review and decode reviewer module for caption generation"
    - "effective approaches to attention based neural machine translation"
    - "aligning where to see and what to tell image caption with region based attention and scene factorization"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for image recognition"
    - "generating images from captions with attention"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"

Connected through hyperedge HE-6 (induced by source paper "automatic attribute discovery with neural activations"):
  with members:
    - "a deep learning based fast image saliency detection algorithm"
    - "understanding neural networks through deep visualization"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "automatic concept discovery from parallel text and visual corpora"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "combining texture and shape cues for object recognition with minimal supervision"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "the new data and new challenges in multimedia research"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "acd action concept discovery from image sentence corpora"):
  with members:
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "contextual action recognition with r cnn"
    - "automatic concept discovery from parallel text and visual corpora"
    - "combining language and vision with a multimodal skip gram model"
    - "very deep convolutional networks for large scale image recognition"
    - "r cnns for pose estimation and action detection"
    - "microsoft coco common objects in context"
    - "actions and attributes from wholes and parts"

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)