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: "effective aesthetics prediction with multi level spatially pooled features"
  Abstract: We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database. While previous approaches miss some of the information in the original images, due to taking small crops, down-scaling or warping the originals during training, we propose the first method that efficiently supports full resolution images as an input, and can be trained on variable input sizes. This allows us to significantly improve upon the state of the art, increasing the Spearman rank-order correlation coefficient (SRCC) of ground-truth mean opinion scores (MOS) from the existing best reported of 0.612 to 0.756. To achieve this performance, we extract multi-level spatially pooled (MLSP) features from all convolutional blocks of a pre-trained InceptionResNet-v2 network, and train a custom shallow Convolutional Neural Network (CNN) architecture on these new features.

Connected through hyperedge HE-1 (induced by source paper "sur featnet predicting the satisfied user ratio curvefor image compression with deep feature learning"):
  with members:
    - "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"
    - "focal loss for dense object detection"
    - "videoset a large scale compressed video quality dataset based on jnd measurement"
    - "prediction of satisfied user ratio for compressed video"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "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"
    - "colorization as a proxy task for visual understanding"

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"
    - "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"
    - "fast at fast automatic thumbnail generation using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "theme aware aesthetic distribution prediction with full resolution photos"):
  with members:
    - "aesthetic driven image enhancement by adversarial learning"
    - "cascade r cnn delving into high quality object detection"
    - "deep aesthetic quality assessment with semantic information"
    - "mask r cnn"
    - "r fcn object detection via region based fully convolutional networks"
    - "fast r cnn"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "no reference video quality assessment using multi level spatially pooled features"):
  with members:
    - "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"
    - "deep learning is robust to massive label noise"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "inception v4 inception resnet and the impact of residual connections on 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)