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: "brain inspired deep networks for image aesthetics assessment"
  Abstract: Image aesthetics assessment has been challenging due to its subjective nature. Inspired by the scientific advances in the human visual perception and neuroaesthetics, we design Brain-Inspired Deep Networks (BDN) for this task. BDN first learns attributes through the parallel supervised pathways, on a variety of selected feature dimensions. A high-level synthesis network is trained to associate and transform those attributes into the overall aesthetics rating. We then extend BDN to predicting the distribution of human ratings, since aesthetics ratings are often subjective. Another highlight is our first-of-its-kind study of label-preserving transformations in the context of aesthetics assessment, which leads to an effective data augmentation approach. Experimental results on the AVA dataset show that our biological inspired and task-specific BDN model gains significantly performance improvement, compared to other state-of-the-art models with the same or higher parameter capacity.

Connected through hyperedge HE-1 (induced by source paper "deep cross networks with aesthetic preference for cross domain recommendation"):
  with members:
    - "conet collaborative cross networks for cross domain recommendation"
    - "aesthetic based clothing recommendation"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "wide deep learning for recommender systems"
    - "cross stitch networks for multi task learning"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "adam a method for stochastic optimization"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "image aesthetics assessment using multi channel convolutional neural networks"):
  with members:
    - "image aesthetic assessment an experimental survey"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "learning a no reference quality assessment model of enhanced images with big data"):
  with members:
    - "learning to rank for blind image quality assessment"

Connected through hyperedge HE-4 (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"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "squared earth mover s distance based loss for training deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "aesthetic based clothing recommendation"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "birank towards ranking on bipartite graphs"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "a visual and textual recurrent neural network for sequential prediction"
    - "describing human aesthetic perception by deeply learned attributes from flickr"

Connected through hyperedge HE-6 (induced by source paper "a scene perception system for visually impaired based on object detection and classification using multi modal dcnn"):
  with members:
    - "noscope optimizing neural network queries over video at scale"
    - "deep feature based contextual model for object detection"
    - "attentive contexts for object detection"
    - "multispectral deep neural networks for pedestrian detection"
    - "fast r cnn"

Connected through hyperedge HE-7 (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"
    - "visual aesthetic quality assessment with multi task deep learning"

Connected through hyperedge HE-8 (induced by source paper "image aesthetic assessment an experimental survey"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "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"

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)