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: "convolutional patch networks with spatial prior for road detection and urban scene understanding"
  Abstract: Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling. We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model. In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset. #R##N#Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.

Connected through hyperedge HE-1 (induced by source paper "active and incremental learning with weak supervision"):
  with members:
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "yolov3 an incremental improvement"
    - "active learning for deep object detection"
    - "focal loss for dense object detection"
    - "cost effective active learning for deep image classification"
    - "extreme clicking for efficient object annotation"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "automatically identifying wild animals in camera trap images with deep learning"

Connected through hyperedge HE-2 (induced by source paper "a methodological review of visual road recognition procedures for autonomous driving applications"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning a driving simulator"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-3 (induced by source paper "an intriguing failing of convolutional neural networks and the coordconv solution"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "distributed prioritized experience replay"
    - "road segmentation using cnn with gru"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "deep image prior"
    - "a simple neural network module for relational reasoning"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-4 (induced by source paper "detection localisation and tracking of pallets using machine learning techniques and 2d range data"):
  with members:
    - "a 2d laser rangefinder scans dataset of standard eur pallets"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "combining lidar space clustering and convolutional neural networks for pedestrian detection"
    - "flexible human robot cooperation models for assisted shop floor tasks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"):
  with members:
    - "anomaly detection in traffic scenes via spatial aware motion reconstruction"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "vision based road detection using contextual blocks"
    - "very deep convolutional networks for large scale image recognition"
    - "road detection by one class color classification dataset and experiments"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "pixel level matching for video object segmentation using convolutional neural networks"):
  with members:
    - "siamese instance search for tracking"
    - "video propagation networks"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "imagenet pre trained models with batch normalization"):
  with members:
    - "impatient dnns deep neural networks with dynamic time budgets"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "dynamic memory networks for visual and textual question answering"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"
    - "vqa visual question answering"

Connected through hyperedge HE-8 (induced by source paper "fine grained recognition in the noisy wild sensitivity analysis of convolutional neural networks approaches"):
  with members:
    - "how well can a cnn marginalize simple nuisances it is designed for"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "hypercolumns for object segmentation and fine grained localization"

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)