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: "discovering beautiful attributes for aesthetic image analysis"
  Abstract: Aesthetic image analysis is the study and assessment of the aesthetic properties of images. Current computational approaches to aesthetic image analysis either provide accurate or interpretable results. To obtain both accuracy and interpretability by humans, we advocate the use of learned and nameable visual attributes as mid-level features. For this purpose, we propose to discover and learn the visual appearance of attributes automatically, using a recently introduced database, called AVA, which contains more than 250,000 images together with their aesthetic scores and textual comments given by photography enthusiasts. We provide a detailed analysis of these annotations as well as the context in which they were given. We then describe how these three key components of AVA - images, scores, and comments - can be effectively leveraged to learn visual attributes. Lastly, we show that these learned attributes can be successfully used in three applications: aesthetic quality prediction, image tagging and retrieval.

Connected through hyperedge HE-1 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-2 (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-3 (induced by source paper "revisiting image aesthetic assessment via self supervised feature learning"):
  with members:
    - "effective aesthetics prediction with multi level spatially pooled features"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "lerop a learning based modular robot photography framework"):
  with members:
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "in defense of the triplet loss for person re identification"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "control of a quadrotor with reinforcement learning"
    - "convolutional pose machines"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "yolo9000 better faster stronger"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"

Connected through hyperedge HE-5 (induced by source paper "engineering deep representations for modeling aesthetic perception"):
  with members:
    - "part based r cnns for fine grained category detection"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "probabilistic latent semantic analysis"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "panda pose aligned networks for deep attribute modeling"
    - "pedestrian detection with unsupervised multi stage feature learning"

Connected through hyperedge HE-6 (induced by source paper "image aesthetics assessment using composite features from off the shelf deep models"):
  with members:
    - "do better imagenet models transfer better"
    - "image aesthetic assessment an experimental survey"
    - "ilgnet inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-7 (induced by source paper "a deep architecture for unified aesthetic prediction"):
  with members:
    - "deep aesthetic quality assessment with semantic information"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "creatism a deep learning photographer capable of creating professional work"):
  with members:
    - "learning to compose with professional photographs on the web"
    - "deep photo style transfer"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "image to image translation with conditional adversarial networks"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "rethinking the inception architecture for computer vision"

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)