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: "fast at fast automatic thumbnail generation using deep neural networks"
  Abstract: Fast-AT is an automatic thumbnail generation system based on deep neural networks. It is a fully-convolutional deep neural network, which learns specific filters for thumbnails of different sizes and aspect ratios. During inference, the appropriate filter is selected depending on the dimensions of the target thumbnail. Unlike most previous work, Fast-AT does not utilize saliency but addresses the problem directly. In addition, it eliminates the need to conduct region search on the saliency map. The model generalizes to thumbnails of different sizes including those with extreme aspect ratios and can generate thumbnails in real time. A data set of more than 70,000 thumbnail annotations was collected to train Fast-AT. We show competitive results in comparison to existing techniques.

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

Connected through hyperedge HE-2 (induced by source paper "user constrained thumbnail generation using adaptive convolutions"):
  with members:
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"
    - "crowd counting by adapting convolutional neural networks with side information"
    - "r fcn object detection via region based fully convolutional networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "context aware saliency detection for image retargeting using convolutional neural networks"):
  with members:
    - "image retargetability"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "weakly and self supervised learning for content aware deep image retargeting"
    - "pyramid scene parsing network"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "pisa pixelwise image saliency by aggregating complementary appearance contrast measures with edge preserving coherence"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "image seam carving by controlling positional distribution of seams"):
  with members:
    - "weakly and self supervised learning for content aware deep image retargeting"

Connected through hyperedge HE-5 (induced by source paper "a2 rl aesthetics aware reinforcement learning for image cropping"):
  with members:
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "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"
    - "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)