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: "a2 rl aesthetics aware reinforcement learning for image cropping"
  Abstract: Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.

Connected through hyperedge HE-1 (induced by source paper "rest performance improvement of a black box model via rl based spatial transformation"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "collaborative deep reinforcement learning for joint object search"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "fine grained video categorization with redundancy reduction attention"
    - "motion feature network fixed motion filter for action recognition"
    - "compact generalized non local network"
    - "adaframe adaptive frame selection for fast video recognition"

Connected through hyperedge HE-3 (induced by source paper "progressive relation learning for group activity recognition"):
  with members:
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "learning actor relation graphs for group activity recognition"
    - "convolutional relational machine for group activity recognition"
    - "a comprehensive survey on graph neural networks"
    - "graph networks as learnable physics engines for inference and control"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "structural recurrent neural network srnn for group activity analysis"
    - "skeleton based action recognition with synchronous local and non local spatio temporal learning and frequency attention"

Connected through hyperedge HE-4 (induced by source paper "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"):
  with members:
    - "single shot temporal action detection"
    - "video captioning via hierarchical reinforcement learning"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "dense captioning events in videos"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-5 (induced by source paper "an end to end neural network for image cropping by learning composition from aesthetic photos"):
  with members:
    - "revisiting salient object detection simultaneous detection ranking and subitizing of multiple salient objects"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "focal loss for dense object detection"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to compose with professional photographs on the web"
    - "deep aesthetic quality assessment with semantic information"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-6 (induced by source paper "unpaired image enhancement featuring reinforcement learning controlled image editing software"):
  with members:
    - "facelet bank for fast portrait manipulation"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "grid anchor based image cropping a new benchmark and an efficient model"):
  with members:
    - "reliable and efficient image cropping a grid anchor based approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "nima neural image assessment"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "image aesthetic assessment an experimental survey"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "aesthetic driven image enhancement by adversarial learning"

Connected through hyperedge HE-8 (induced by source paper "image cropping with composition and saliency aware aesthetic score map"):
  with members:
    - "listwise view ranking for image cropping"
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "reliable and efficient image cropping a grid anchor based approach"
    - "an end to end neural network for image cropping by learning composition from aesthetic photos"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "learning to compose with professional photographs on the web"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "fast at fast automatic thumbnail generation using deep neural 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)