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: "deep aesthetic quality assessment with semantic information"
  Abstract: Human beings often assess the aesthetic quality of an image coupled with the identification of the image’s semantic content. This paper addresses the correlation issue between automatic aesthetic quality assessment and semantic recognition. We cast the assessment problem as the main task among a multi-task deep model, and argue that semantic recognition task offers the key to address this problem. Based on convolutional neural networks, we employ a single and simple multi-task framework to efficiently utilize the supervision of aesthetic and semantic labels. A correlation item between these two tasks is further introduced to the framework by incorporating the inter-task relationship learning. This item not only provides some useful insight about the correlation but also improves assessment accuracy of the aesthetic task. In particular, an effective strategy is developed to keep a balance between the two tasks, which facilitates to optimize the parameters of the framework. Extensive experiments on the challenging Aesthetic Visual Analysis dataset and Photo.net dataset validate the importance of semantic recognition in aesthetic quality assessment, and demonstrate that multitask deep models can discover an effective aesthetic representation to achieve the state-of-the-art results.

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"
    - "image aesthetic assessment an experimental survey"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

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"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "learning to compose with professional photographs on the web"
    - "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 "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"
    - "image aesthetic assessment an experimental survey"
    - "convolutional image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-4 (induced by source paper "theme aware aesthetic distribution prediction with full resolution photos"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "aesthetic driven image enhancement by adversarial learning"
    - "cascade r cnn delving into high quality object detection"
    - "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 "neural aesthetic image reviewer"):
  with members:
    - "image aesthetic assessment an experimental survey"
    - "image captioning with semantic attention"
    - "boosting image captioning with attributes"
    - "senticap generating image descriptions with sentiments"
    - "what value do explicit high level concepts have in vision to language problems"
    - "densecap fully convolutional localization networks for dense captioning"
    - "microsoft coco captions data collection and evaluation server"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "a gated peripheral foveal convolutional neural network for unified image aesthetic prediction"):
  with members:
    - "deep visual attention prediction"
    - "nima neural image assessment"
    - "robust deep multi modal learning based on gated information fusion network"
    - "a deep architecture for unified aesthetic prediction"
    - "learned perceptual image enhancement"
    - "predicting aesthetic score distribution through cumulative jensen shannon divergence"
    - "squeeze and excitation networks"
    - "top down neural attention by excitation backprop"

Connected through hyperedge HE-7 (induced by source paper "aesthetics assessment of images containing faces"):
  with members:
    - "on the use of deep learning for blind image quality assessment"
    - "affact alignment free facial attribute classification technique"
    - "user preferences modeling and learning for pleasing photo collage generation"
    - "fast r cnn"

Connected through hyperedge HE-8 (induced by source paper "a deep architecture for unified aesthetic prediction"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "learning without forgetting"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "deep residual learning for image recognition"
    - "understanding deep image representations by inverting them"
    - "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)