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: "attend to you personalized image captioning with context sequence memory networks"
  Abstract: We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the user's active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1M posts from 6.3K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.

Connected through hyperedge HE-1 (induced by source paper "aitpr attribute interaction tensor product representation for image caption"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "multimodal image captioning for marketing analysis"
    - "pragmatically informative image captioning with character level reference"

Connected through hyperedge HE-2 (induced by source paper "mrrc multiple role representation crossover interpretation for image captioning with r cnn feature distribution composition fdc"):
  with members:
    - "learning to guide decoding for image captioning"
    - "neural baby talk"
    - "coco cn for cross lingual image tagging captioning and retrieval"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "attentive tensor product learning"
    - "pragmatically informative image captioning with character level reference"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"

Connected through hyperedge HE-3 (induced by source paper "gaussian smoothen semantic features gssf exploring the linguistic aspects of visual captioning in indian languages bengali using mscoco framework"):
  with members:
    - "neural baby talk"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "pragmatically informative image captioning with character level reference"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"
    - "improved image captioning via policy gradient optimization of spider"
    - "actor critic sequence training for image captioning"
    - "deep reinforcement learning based image captioning with embedding reward"

Connected through hyperedge HE-4 (induced by source paper "audio driven talking face video generation with learning based personalized head pose"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "neural voice puppetry audio driven facial reenactment"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "lip movements generation at a glance"

Connected through hyperedge HE-5 (induced by source paper "audio driven talking face video generation with natural head pose"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "few shot adversarial learning of realistic neural talking head models"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "talking face generation by adversarially disentangled audio visual representation"

Connected through hyperedge HE-6 (induced by source paper "transferring cross domain knowledge for video sign language recognition"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "word level deep sign language recognition from video a new large scale dataset and methods comparison"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "ms asl a large scale data set and benchmark for understanding american sign language"
    - "neural sign language translation based on human keypoint estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "reasoning visual dialogs with structural and partial observations"):
  with members:
    - "flipdial a generative model for two way visual dialogue"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "learning visual question answering by bootstrapping hard attention"
    - "discriminability objective for training descriptive captions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning human object interactions by graph parsing neural networks"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"

Connected through hyperedge HE-8 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"

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)