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: "deep cropping via attention box prediction and aesthetics assessment"
  Abstract: We model the photo cropping problem as a cascade of attention box regression and aesthetic quality classification, based on deep learning. A neural network is designed that has two branches for predicting attention bounding box and analyzing aesthetics, respectively. The predicted attention box is treated as an initial crop window where a set of cropping candidates are generated around it, without missing important information. Then, aesthetics assessment is employed to select the final crop as the one with the best aesthetic quality. With our network, cropping candidates share features within full-image convolutional feature maps, thus avoiding repeated feature computation and leading to higher computation efficiency. Via leveraging rich data for attention prediction and aesthetics assessment, the proposed method produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results demonstrate the competitive results and fast processing speed (5 fps with all steps).

Connected through hyperedge HE-1 (induced by source paper "composition aware image aesthetics assessment"):
  with members:
    - "nima neural image assessment"
    - "videos as space time region graphs"
    - "a deep architecture for unified aesthetic prediction"
    - "non local neural networks"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "attend and interact higher order object interactions for video understanding"
    - "image aesthetic assessment an experimental survey"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-2 (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"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-3 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "high fidelity image generation with fewer labels"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "aesthetic based clothing recommendation"
    - "tracking emerges by colorizing videos"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"

Connected through hyperedge HE-4 (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-5 (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"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"
    - "fast at fast automatic thumbnail generation using deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "reliable and efficient image cropping a grid anchor based approach"):
  with members:
    - "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"
    - "learning to compose with professional photographs on the web"
    - "mask r cnn"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "image retargetability"):
  with members:
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to compose with professional photographs on the web"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning to compare image patches via convolutional neural networks"
    - "multi task cnn model for attribute prediction"
    - "mirror mirror on the wall tell me is the error small"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "listwise view ranking for image cropping"):
  with members:
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "learning to compose with professional photographs on the web"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "instance aware semantic segmentation via multi task network cascades"
    - "fast r cnn"

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)