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: "skeleton key image captioning by skeleton attribute decomposition"
  Abstract: Recently, there has been a lot of interest in automatically generating descriptions for an image. Most existing language-model based approaches for this task learn to generate an image description word by word in its original word order. However, for humans, it is more natural to locate the objects and their relationships first, and then elaborate on each object, describing notable attributes. We present a coarse-to-fine method that decomposes the original image description into a skeleton sentence and its attributes, and generates the skeleton sentence and attribute phrases separately. By this decomposition, our method can generate more accurate and novel descriptions than the previous state-of-the-art. Experimental results on the MS-COCO and a larger scale Stock3M datasets show that our algorithm yields consistent improvements across different evaluation metrics, especially on the SPICE metric, which has much higher correlation with human ratings than the conventional metrics. Furthermore, our algorithm can generate descriptions with varied length, benefiting from the separate control of the skeleton and attributes. This enables image description generation that better accommodates user preferences.

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 "crur coupled recurrent unit for unification conceptualization and context capture for language representation a generalization of bi directional lstm"):
  with members:
    - "learning to guide decoding for image captioning"
    - "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"
    - "adversarial semantic alignment for improved image captions"
    - "entity aware image caption generation"
    - "joint image captioning and question answering"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-5 (induced by source paper "a deep decoder structure based on wordembedding regression for an encoder decoder based model for image captioning"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "stack captioning coarse to fine learning for image captioning"
    - "attend to you personalized image captioning with context sequence memory networks"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "semantic compositional networks for visual captioning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-6 (induced by source paper "exploring overall contextual information for image captioning in human like cognitive style"):
  with members:
    - "knowledge embedded representation learning for fine grained image recognition"
    - "neural baby talk"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "rethinking the form of latent states in image captioning"
    - "learning to parse and translate improves neural machine translation"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "convolutional image captioning"
    - "an analysis of scale invariance in object detection snip"

Connected through hyperedge HE-7 (induced by source paper "generation of multimodal justification using visual word constraint model for explainable computer aided diagnosis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "brain tumor segmentation with deep neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

Connected through hyperedge HE-8 (induced by source paper "masked non autoregressive image captioning"):
  with members:
    - "a neural compositional paradigm for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "convolutional image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "spice semantic propositional image caption evaluation"

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)