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: "fully convolutional network with multi step reinforcement learning for image processing"
  Abstract: This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep RL for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied. We apply the proposed method to three image processing tasks: image denoising, image restoration, and local color enhancement. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised video summarization by hierarchical reinforcement learning"):
  with members:
    - "rethinking the evaluation of video summaries"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deep reinforcement learning for visual object tracking in videos"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-2 (induced by source paper "federated deep reinforcement learning"):
  with members:
    - "federated machine learning concept and applications"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "ai online filters to real world image recognition"):
  with members:
    - "online learning to estimate warfarin dose with contextual linear bandits"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "deep reinforcement learning with double q learning"
    - "active object localization with deep reinforcement learning"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing"):
  with members:
    - "weakly supervised video summarization by hierarchical reinforcement learning"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "ffnet video fast forwarding via reinforcement learning"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "hierarchical cellular automata for visual saliency"

Connected through hyperedge HE-5 (induced by source paper "unpaired image enhancement featuring reinforcement learning controlled image editing software"):
  with members:
    - "facelet bank for fast portrait manipulation"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deep bilateral learning for real time image enhancement"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "iteratively refined interactive 3d medical image segmentation with multi agent reinforcement learning"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "unet a nested u net architecture for medical image segmentation"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "iterative interaction training for segmentation editing networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "annotating object instances with a polygon rnn"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "federated reinforcement learning"):
  with members:
    - "federated machine learning concept and applications"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-8 (induced by source paper "convolutional neural network with median layers for denoising salt and pepper contaminations"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "noise2noise learning image restoration without clean data"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "image super resolution using deep convolutional networks"

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)