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: "visual aesthetic quality assessment with multi task deep learning"
  Abstract: This paper considers the problem of assessing visual aesthetic quality with semantic information. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition offers the key to addressing this problem. Based on convolutional neural networks, we propose a general multi-task framework with four different structures. In each structure, aesthetic quality assessment task and semantic recognition task are leveraged, and different features are explored to improve the quality assessment. Moreover, an effective strategy of keeping a balanced effect between the semantic task and aesthetic task is developed to optimize the parameters of our framework. The correlation analysis among the tasks validates the importance of the semantic recognition in aesthetic quality assessment. Extensive experiments verify the effectiveness of the proposed multi-task framework, and further corroborate the above proposition.

Connected through hyperedge HE-1 (induced by source paper "cross connected networks for multi task learning of detection and segmentation"):
  with members:
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "blitznet a real time deep network for scene understanding"
    - "learning to segment every thing"
    - "multi task self supervised visual learning"
    - "segflow joint learning for video object segmentation and optical flow"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "nima neural image assessment"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "on the use of deep learning for blind image quality assessment"
    - "learned perceptual image enhancement"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a color intensity invariant low level feature optimization framework for image quality assessment"
    - "brain inspired deep networks for image aesthetics assessment"
    - "squared earth mover s distance based loss for training deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "learned perceptual image enhancement"):
  with members:
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "style transfer via texture synthesis"
    - "fast image processing with fully convolutional networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "brain inspired deep networks for image aesthetics assessment"

Connected through hyperedge HE-4 (induced by source paper "image aesthetic assessment an experimental survey"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "brain inspired deep networks for image aesthetics assessment"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "describing human aesthetic perception by deeply learned attributes from flickr"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "ilgnet inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a joint convolutional neural networks and context transfer for street scenes labeling"
    - "deep aesthetic quality assessment with semantic information"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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)