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: "image aesthetic assessment an experimental survey"
  Abstract: This article reviews recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality from low-quality photos based on photographic rules, typically in the form of binary classification or quality scoring. A variety of approaches has been proposed in the literature to try to solve this challenging problem. In this article, we summarize these approaches based on visual feature types (hand-crafted features and deep features) and evaluation criteria (data set characteristics and evaluation metrics). The main contributions and novelties of the reviewed approaches are highlighted and discussed. In addition, following the emergence of deep-learning techniques, we systematically evaluate recent deep-learning settings that are useful for developing a robust deep model for aesthetic scoring.

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"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-2 (induced by source paper "design and evaluation of product aesthetics a human machine hybrid approach"):
  with members:
    - "3d shape synthesis for conceptual design and optimization using variational autoencoders"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "learning type aware embeddings for fashion compatibility"
    - "design design inspiration from generative networks"
    - "pioneer networks progressively growing generative autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "aesthetic attributes assessment of images"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "nima neural image assessment"
    - "neural aesthetic image reviewer"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "convolutional image captioning"
    - "deep aesthetic quality assessment with semantic information"
    - "bottom up and top down attention for image captioning and visual question answering"

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"
    - "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-5 (induced by source paper "which ads to show advertisement image assessment with auxiliary information via multi step modality fusion"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "character region awareness for text detection"
    - "multimodal dual attention memory for video story question answering"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "aesthetic based clothing recommendation"
    - "nsml meet the mlaas platform with a real world case study"

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"
    - "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-7 (induced by source paper "personalised aesthetics with residual adapters"):
  with members:
    - "fast perceptual image enhancement"
    - "aesthetic based clothing recommendation"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "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-8 (induced by source paper "evaluating artificial systems for pairwise ranking tasks sensitive to individual differences"):
  with members:
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "deep residual learning for image recognition"
    - "integrating deep features for material recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale 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)