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: "rich image captioning in the wild"
  Abstract: We present an image caption system that addresses new challenges of automatically describing images in the wild. The challenges include high quality caption quality with respect to human judgments, out-of-domain data handling, and low latency required in many applications. Built on top of a state-of-the-art framework, we developed a deep vision model that detects a broad range of visual concepts, an entity recognition model that identifies celebrities and landmarks, and a confidence model for the caption output. Experimental results show that our caption engine outperforms previous state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO) and out of-domain datasets.

Connected through hyperedge HE-1 (induced by source paper "vision and language from visual perception to content creation"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "hierarchy parsing for image captioning"
    - "unsupervised person image generation with semantic parsing transformation"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "generating multiple objects at spatially distinct locations"
    - "joint discriminative and generative learning for person re identification"
    - "relationship aware spatial perception fusion for realistic scene layout generation"
    - "photo realistic facial details synthesis from single image"

Connected through hyperedge HE-2 (induced by source paper "v prom a benchmark for visual reasoning using visual progressive matrices"):
  with members:
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"
    - "learning generalized reactive policies using deep neural networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "an analysis of visual question answering algorithms"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning robust rewards with adversarial inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "good news everyone context driven entity aware captioning for news images"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "entity aware image caption generation"
    - "learning to evaluate image captioning"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "spice semantic propositional image caption evaluation"
    - "re evaluating automatic metrics for image captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-4 (induced by source paper "semantic comparison of state of the art deep learning methods for image multi label classification"):
  with members:
    - "reasonet learning to stop reading in machine comprehension"
    - "mask r cnn"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "cnn rnn a unified framework for multi label image classification"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "partially supervised image captioning"):
  with members:
    - "neural baby talk"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "polyglot semantic parsing in apis"
    - "attention is all you need"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-6 (induced by source paper "unsupervised image captioning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "large scale gan training for high fidelity natural image synthesis"
    - "recurrent fusion network for image captioning"
    - "unpaired image captioning by language pivoting"
    - "phrase based neural unsupervised machine translation"
    - "partially supervised image captioning"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-7 (induced by source paper "entity aware image caption generation"):
  with members:
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "captioning images with diverse objects"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "very deep convolutional networks for large scale image recognition"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-8 (induced by source paper "knowledge rich image gist understanding beyond literal meaning"):
  with members:
    - "exploring representativeness and informativeness for active learning"
    - "exploring models and data for image question answering"
    - "video in sentences out"
    - "imagenet large scale visual recognition challenge"
    - "from captions to visual concepts and back"
    - "microsoft coco common objects in context"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

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)