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: "a comprehensive survey of deep learning for image captioning"
  Abstract: Generating a description of an image is called image captioning. Image captioning requires to recognize the important objects, their attributes and their relationships in an image. It also needs to generate syntactically and semantically correct sentences. Deep learning-based techniques are capable of handling the complexities and challenges of image captioning. In this survey paper, we aim to present a comprehensive review of existing deep learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep learning based automatic image captioning.

Connected through hyperedge HE-1 (induced by source paper "language is power representing states using natural language in reinforcement learning"):
  with members:
    - "hierarchical decision making by generating and following natural language instructions"
    - "action assembly sparse imitation learning for text based games with combinatorial action spaces"
    - "the natural language of actions"
    - "adaptive discretization for episodic reinforcement learning in metric spaces"
    - "textworld a learning environment for text based games"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "emergence of invariance and disentanglement in deep representations"
    - "do latent tree learning models identify meaningful structure in sentences"

Connected through hyperedge HE-2 (induced by source paper "captioning images taken by people who are blind"):
  with members:
    - "attention on attention for image captioning"
    - "nocaps novel object captioning at scale"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "auto encoding scene graphs for image captioning"
    - "engaging image captioning via personality"
    - "speech coco 600k visually grounded spoken captions aligned to mscoco data set"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "stair captions constructing a large scale japanese image caption dataset"

Connected through hyperedge HE-3 (induced by source paper "uit viic a dataset for the first evaluation on vietnamese image captioning"):
  with members:
    - "deep residual learning for image recognition"
    - "microsoft coco captions data collection and evaluation server"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "imagenet large scale visual recognition challenge"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-4 (induced by source paper "image captioning using facial expression and attention"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "face cap image captioning using facial expression analysis"
    - "automatic recognition of student engagement using deep learning and facial expression"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attributed social network embedding"
    - "characterizing and detecting hateful users on twitter"

Connected through hyperedge HE-6 (induced by source paper "text to image synthesis based on machine generated captions"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "mirrorgan learning text to image generation by redescription"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "actor critic sequence training for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-7 (induced by source paper "better understanding hierarchical visual relationship for image caption"):
  with members:
    - "selective attention based graph convolutional networks for aspect level sentiment classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "person re identification with deep similarity guided graph neural network"
    - "scaling neural machine translation"
    - "videos as space time region graphs"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-8 (induced by source paper "orderless recurrent models for multi label classification"):
  with members:
    - "elucidating image to set prediction an analysis of models losses and datasets"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "averaging weights leads to wider optima and better generalization"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"

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)