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: "understanding and mapping natural beauty"
  Abstract: While natural beauty is often considered a subjective property of images, in this paper, we take an objective approach and provide methods for quantifying and predicting the scenicness of an image. Using a dataset containing hundreds of thousands of outdoor images captured throughout Great Britain with crowdsourced ratings of natural beauty, we propose an approach to predict scenicness which explicitly accounts for the variance of human ratings. We demonstrate that quantitative measures of scenicness can benefit semantic image understanding, content-aware image processing, and a novel application of cross-view mapping, where the sparsity of ground-level images can be addressed by incorporating unlabeled overhead images in the training and prediction steps. For each application, our methods for scenicness prediction result in quantitative and qualitative improvements over baseline approaches.

Connected through hyperedge HE-1 (induced by source paper "semantically interpretable activation maps what where how explanations within cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gradmask reduce overfitting by regularizing saliency"
    - "interpretability beyond classification output semantic bottleneck networks"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "methods for interpreting and understanding deep neural networks"
    - "semantic bottleneck for computer vision tasks"

Connected through hyperedge HE-2 (induced by source paper "farsa fully automated roadway safety assessment"):
  with members:
    - "a unified model for near and remote sensing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep learning the city quantifying urban perception at a global scale"
    - "torontocity seeing the world with a million eyes"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-3 (induced by source paper "totally looks like how humans compare compared to machines"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "modeling human categorization of natural images using deep feature representations"
    - "recent advance in content based image retrieval a literature survey"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "adapting deep network features to capture psychological representations"
    - "densely connected convolutional networks"
    - "understanding image virality"
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "what goes where predicting object distributions from above"):
  with members:
    - "deep learning in remote sensing a review"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "predicting ground level scene layout from aerial imagery"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "a unified model for near and remote sensing"):
  with members:
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "deep learning the city quantifying urban perception at a global scale"
    - "torontocity seeing the world with a million eyes"
    - "land use classification using convolutional neural networks applied to ground level images"
    - "predicting ground level scene layout from aerial imagery"
    - "snowwatch snow monitoring through acquisition and analysis of user generated content"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)