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: "scene parsing with multiscale feature learning purity trees and optimal covers"
  Abstract: Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. #R##N#The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained end-to-end from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. #R##N#The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 \times 240 image labeling in less than 1 second.

Connected through hyperedge HE-1 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks"):
  with members:
    - "deep network approximation for smooth functions"
    - "the troublesome kernel why deep learning for inverse problems is typically unstable"
    - "greedy shallow networks a new approach for constructing and training neural networks"
    - "deep double descent where bigger models and more data hurt"
    - "growing axons greedy learning of neural networks with application to function approximation"
    - "a polynomial based approach for architectural design and learning with deep neural networks"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "neural network integral representations with the relu activation function"

Connected through hyperedge HE-2 (induced by source paper "self supervised model adaptation for multimodal semantic segmentation"):
  with members:
    - "robot localization in floor plans using a room layout edge extraction network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "in defense of classical image processing fast depth completion on the cpu"

Connected through hyperedge HE-3 (induced by source paper "multimodal recurrent neural networks with information transfer layers for indoor scene labeling"):
  with members:
    - "perceptual generative adversarial networks for small object detection"
    - "beyond forward shortcuts fully convolutional master slave networks msnets with backward skip connections for semantic segmentation"
    - "robust face recognition via multimodal deep face representation"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "cross modal distillation for supervision transfer"
    - "multi task cnn model for attribute prediction"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "wrapped loss function for regularizing nonconforming residual distributions"):
  with members:
    - "dnn and cnn with weighted and multi task loss functions for audio event detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

Connected through hyperedge HE-5 (induced by source paper "single image super resolution with dilated convolution based multi scale information learning inception module"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "accurate image super resolution using very deep convolutional networks"
    - "multi scale context aggregation by dilated convolutions"
    - "compression artifacts reduction by a deep convolutional network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep networks for image super resolution with sparse prior"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "deep predictive models for collision risk assessment in autonomous driving"):
  with members:
    - "aggressive deep driving model predictive control with a cnn cost model"
    - "an empirical evaluation of deep learning on highway driving"
    - "joint belief and intent prediction for collision avoidance in autonomous vehicles"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning for classification of colorectal polyps on whole slide images"):
  with members:
    - "deep residual learning for image recognition"
    - "assessment of algorithms for mitosis detection in breast cancer histopathology images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-8 (induced by source paper "detection recognition and tracking of moving objects from real time video via sp theory of intelligence and species inspired pso"):
  with members:
    - "unifying computing and cognition the sp theory and its applications"

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)