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: "hybrid retrieval generation reinforced agent for medical image report generation"
  Abstract: Generating long and coherent reports to describe medical images poses challenges to bridging visual patterns with informative human linguistic descriptions. We propose a novel Hybrid Retrieval-Generation Reinforced Agent (HRGR-Agent) which reconciles traditional retrieval-based approaches populated with human prior knowledge, with modern learning-based approaches to achieve structured, robust, and diverse report generation. HRGR-Agent employs a hierarchical decision-making procedure. For each sentence, a high-level retrieval policy module chooses to either retrieve a template sentence from an off-the-shelf template database, or invoke a low-level generation module to generate a new sentence. HRGR-Agent is updated via reinforcement learning, guided by sentence-level and word-level rewards. Experiments show that our approach achieves the state-of-the-art results on two medical report datasets, generating well-balanced structured sentences with robust coverage of heterogeneous medical report contents. In addition, our model achieves the highest detection accuracy of medical terminologies, and improved human evaluation performance.

Connected through hyperedge HE-1 (induced by source paper "clara clinical report auto completion"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "neural baby talk"
    - "a neural compositional paradigm for image captioning"
    - "personalized language model for query auto completion"
    - "on the automatic generation of medical imaging reports"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "learning to summarize radiology findings"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning applications"):
  with members:
    - "coloring big graphs with alphagozero"
    - "learning agile and dynamic motor skills for legged robots"
    - "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (induced by source paper "what makes a good story designing composite rewards for visual storytelling"):
  with members:
    - "uniter learning universal image text representations"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "plan and write towards better automatic storytelling"
    - "learning neural templates for text generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "hierarchical neural story generation"

Connected through hyperedge HE-4 (induced by source paper "addressing data bias problems for chest x ray image report generation"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "vispi automatic visual perception and interpretation of chest x rays"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "on the automatic generation of medical imaging reports"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "self critical sequence training for image captioning"

Connected through hyperedge HE-6 (induced by source paper "texar a modularized versatile and extensible toolkit for text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "target guided open domain conversation"
    - "deepmind control suite"
    - "unsupervised text style transfer using language models as discriminators"
    - "tensor2tensor for neural machine translation"
    - "deep generative models with learnable knowledge constraints"
    - "xnmt the extensible neural machine translation toolkit"
    - "marian fast neural machine translation in c"

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)