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: "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
  Abstract: Despite recent progress, computational visual aesthetic is still challenging. Image cropping, which refers to the removal of unwanted scene areas, is an important step to improve the aesthetic quality of an image. However, it is challenging to evaluate whether cropping leads to aesthetically pleasing results because the assessment is typically subjective. In this paper, we propose a novel cascaded cropping regression (CCR) method to perform image cropping by learning the knowledge from professional photographers. The proposed CCR method improves the convergence speed of the cascaded method, which directly uses random-ferns regressors. In addition, a two-step learning strategy is proposed and used in the CCR method to address the problem of lacking labelled cropping data. Specifically, a deep convolutional neural network (CNN) classifier is first trained on large-scale visual aesthetic datasets. The deep CNN model is then designed to extract features from several image cropping datasets, upon which the cropping bounding boxes are predicted by the proposed CCR method. Experimental results on public image cropping datasets demonstrate that the proposed method significantly outperforms several state-of-the-art image cropping methods.

Connected through hyperedge HE-1 (induced by source paper "quality aware unpaired image to image translation"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "perceptual adversarial networks for image to image transformation"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "undeepvo monocular visual odometry through unsupervised deep learning"

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"
    - "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-3 (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"
    - "aesthetic driven image enhancement by adversarial learning"
    - "learning to compose with professional photographs on the web"

Connected through hyperedge HE-4 (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"
    - "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 "image retargetability"):
  with members:
    - "learning to compose with professional photographs on the web"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "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-6 (induced by source paper "a fast face detection method via convolutional neural network"):
  with members:
    - "object discovery via cohesion measurement"
    - "scale aware face detection"
    - "face detection using deep learning an improved faster rcnn approach"
    - "face detection with the faster r cnn"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "you only look once unified real time object detection"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "deep residual learning for image recognition"

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)