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: "phrase based image captioning"
  Abstract: Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO.

Connected through hyperedge HE-1 (induced by source paper "video captioning with text based dynamic attention and step by step learning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "memory attended recurrent network for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "reconstruction network for video captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "saliency guided video classification via adaptively weighted learning"
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "hierarchical boundary aware neural encoder for video captioning"

Connected through hyperedge HE-2 (induced by source paper "a strong and robust baseline for text image matching"):
  with members:
    - "good news everyone context driven entity aware captioning for news images"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "foil it find one mismatch between image and language caption"
    - "learning two branch neural networks for image text matching tasks"
    - "vse improving visual semantic embeddings with hard negatives"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"

Connected through hyperedge HE-3 (induced by source paper "end to end image captioning exploits multimodal distributional similarity"):
  with members:
    - "object counts bringing explicit detections back into image captioning"
    - "obj2text generating visually descriptive language from object layouts"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "yolo9000 better faster stronger"
    - "boosting image captioning with attributes"
    - "self critical sequence training for image captioning"
    - "language models for image captioning the quirks and what works"
    - "what value do explicit high level concepts have in vision to language problems"

Connected through hyperedge HE-4 (induced by source paper "bootstrapping generators from noisy data"):
  with members:
    - "handling divergent reference texts when evaluating table to text generation"
    - "sentence simplification with deep reinforcement learning"
    - "what does attention in neural machine translation pay attention to"
    - "learning to generate one sentence biographies from wikidata"
    - "order planning neural text generation from structured data"
    - "challenges in data to document generation"
    - "table to text generation by structure aware seq2seq learning"
    - "semantic tagging with deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "image captioning with semantic attention"
    - "attention correctness in neural image captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "senticap generating image descriptions with sentiments"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"

Connected through hyperedge HE-6 (induced by source paper "video captioning with guidance of multimodal latent topics"):
  with members:
    - "generating video descriptions with topic guidance"
    - "weakly supervised dense video captioning"
    - "improving interpretability of deep neural networks with semantic information"
    - "semantic compositional networks for visual captioning"
    - "review networks for caption generation"
    - "image captioning with semantic attention"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "frame and segment level features and candidate pool evaluation for video caption generation"

Connected through hyperedge HE-7 (induced by source paper "skeleton key image captioning by skeleton attribute decomposition"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "phi lstm a phrase based hierarchical lstm model for image captioning"
    - "language models for image captioning the quirks and what works"
    - "what value do explicit high level concepts have in vision to language problems"
    - "deep residual learning for image recognition"
    - "exploring nearest neighbor approaches for image captioning"

Connected through hyperedge HE-8 (induced by source paper "teaching machines to describe images via natural language feedback"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "the burchak corpus a challenge data set for interactive learning of visually grounded word meanings"
    - "beating atari with natural language guided reinforcement learning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "deep reinforcement learning for dialogue generation"
    - "dialog based language learning"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "training an adaptive dialogue policy for interactive learning of visually grounded word meanings"

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.CL(Computation and Language)