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: "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
  Abstract: Automatic photo cropping is an important tool for improving visual quality of digital photos without resorting to tedious manual selection. Traditionally, photo cropping is accomplished by determining the best proposal window through visual quality assessment or saliency detection. In essence, the performance of an image cropper highly depends on the ability to correctly rank a number of visually similar proposal windows. Despite the ranking nature of automatic photo cropping, little attention has been paid to learning-to-rank algorithms in tackling such a problem. In this work, we conduct an extensive study on traditional approaches as well as ranking-based croppers trained on various image features. In addition, a new dataset consisting of high quality cropping and pairwise ranking annotations is presented to evaluate the performance of various baselines. The experimental results on the new dataset provide useful insights into the design of better photo cropping algorithms.

Connected through hyperedge HE-1 (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"
    - "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"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "learning to compose with professional photographs on the web"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"
    - "fast at fast automatic thumbnail generation using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "reliable and efficient image cropping a grid anchor based approach"):
  with members:
    - "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"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "mask r cnn"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "listwise view ranking for image cropping"):
  with members:
    - "learning to compose with professional photographs on the web"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "instance aware semantic segmentation via multi task network cascades"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "personalised aesthetics with residual adapters"):
  with members:
    - "fast perceptual image enhancement"
    - "aesthetic based clothing recommendation"
    - "image aesthetic assessment an experimental survey"
    - "learning multiple visual domains with residual adapters"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "some like it hot visual guidance for preference prediction"

Connected through hyperedge HE-7 (induced by source paper "aesthetic driven image enhancement by adversarial learning"):
  with members:
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "image aesthetic assessment an experimental survey"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to compose with professional photographs on the web"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "characterizing and improving stability in neural style transfer"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "towards diverse and natural image descriptions via a conditional gan"

Connected through hyperedge HE-8 (induced by source paper "a2 rl aesthetics aware reinforcement learning for image cropping"):
  with members:
    - "image aesthetic assessment an experimental survey"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "learning to compose with professional photographs on the web"
    - "weakly and self supervised learning for content aware deep image retargeting"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "fast at fast automatic thumbnail generation using deep neural networks"
    - "asynchronous methods for deep reinforcement learning"

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)