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: "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
  Abstract: Deep convolutional neural networks (CNN) have recently been shown to generate promising results for aesthetics assessment. However, the performance of these deep CNN methods is often compromised by the constraint that the neural network only takes the fixed-size input. To accommodate this requirement, input images need to be transformed via cropping, warping, or padding, which often alter image composition, reduce image resolution, or cause image distortion. Thus the aesthetics of the original images is impaired because of potential loss of fine grained details and holistic image layout. However, such fine grained details and holistic image layout is critical for evaluating an image's aesthetics. In this paper, we present an Adaptive Layout-Aware Multi-Patch Convolutional Neural Network (A-Lamp CNN) architecture for photo aesthetic assessment. This novel scheme is able to accept arbitrary sized images, and learn from both fined grained details and holistic image layout simultaneously. To enable training on these hybrid inputs, we extend the method by developing a dedicated double-subnet neural network structure, i.e. a Multi-Patch subnet and a Layout-Aware subnet. We further construct an aggregation layer to effectively combine the hybrid features from these two subnets. Extensive experiments on the large-scale aesthetics assessment benchmark (AVA) demonstrate significant performance improvement over the state-of-the-art in photo aesthetic assessment.

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"
    - "visual translation embedding network for visual relation detection"

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 "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 aesthetic quality assessment with semantic information"
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "a2 rl aesthetics aware reinforcement learning for image cropping"

Connected through hyperedge HE-4 (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-5 (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"
    - "deep aesthetic quality assessment with semantic information"

Connected through hyperedge HE-6 (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"
    - "wide deep learning for recommender systems"
    - "cross stitch networks for multi task learning"
    - "brain inspired deep networks for image aesthetics assessment"
    - "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-7 (induced by source paper "visually aware recommendation with aesthetic features"):
  with members:
    - "nais neural attentive item similarity model for recommendation"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "large scale visual recommendations from street fashion images"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"

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)