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: "novel deep learning model for traffic sign detection using capsule networks"
  Abstract: Convolutional neural networks are the most widely used deep learning algorithms for traffic signal classification till date but they fail to capture pose, view, orientation of the images because of the intrinsic inability of max pooling layer.This paper proposes a novel method for Traffic sign detection using deep learning architecture called capsule networks that achieves outstanding performance on the German traffic sign dataset.Capsule network consists of capsules which are a group of neurons representing the instantiating parameters of an object like the pose and orientation by using the dynamic routing and route by agreement algorithms.unlike the previous approaches of manual feature extraction,multiple deep neural networks with many parameters,our method eliminates the manual effort and provides resistance to the spatial variances.CNNs can be fooled easily using various adversary attacks and capsule networks can overcome such attacks from the intruders and can offer more reliability in traffic sign detection for autonomous vehicles.Capsule network have achieved the state-of-the-art accuracy of 97.6% on German Traffic Sign Recognition Benchmark dataset (GTSRB).

Connected through hyperedge HE-1 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "videocapsulenet a simplified network for action detection"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-2 (induced by source paper "capsule network performance with autonomous navigation"):
  with members:
    - "capsule neural network based height classification using low cost automotive ultrasonic sensors"
    - "advanced capsule networks via context awareness"
    - "deep reinforcement learning"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "simultaneous localization and mapping with depth prediction using capsule networks for uavs"
    - "state representation learning with recurrent capsule networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "enabling highly efficient capsule networks processing through a pim based architecture design"):
  with members:
    - "optimally scheduling cnn convolutions for efficient memory access"
    - "capsule neural network based height classification using low cost automotive ultrasonic sensors"
    - "high throughput cnn inference on embedded arm big little multi core processors"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "capsnet comparative performance evaluation for image classification"
    - "fast capsnet for lung cancer screening"
    - "origami a heterogeneous split architecture for in memory acceleration of learning"

Connected through hyperedge HE-4 (induced by source paper "capsattacks robust and imperceptible adversarial attacks on capsule networks"):
  with members:
    - "towards imperceptible and robust adversarial example attacks against neural networks"
    - "capsnet comparative performance evaluation for image classification"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "are adversarial examples inevitable"
    - "dolphinattack inaudible voice commands"
    - "enhancing robustness of machine learning systems via data transformations"
    - "dynamic routing between capsules"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "improving performance and inference on audio classification tasks using capsule networks"):
  with members:
    - "brain tumor type classification via capsule networks"
    - "capsule routing for sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "dynamic routing between capsules"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "large scale weakly supervised audio classification using gated convolutional neural network"
    - "musan a music speech and noise corpus"

Connected through hyperedge HE-6 (induced by source paper "total recall understanding traffic signs using deep hierarchical convolutional neural networks"):
  with members:
    - "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "efficient yet deep convolutional neural networks for semantic segmentation"
    - "parsenet looking wider to see better"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "vehicle local position estimation system"
    - "a novel plsa based traffic signs classification system"

Connected through hyperedge HE-7 (induced by source paper "a brief survey on autonomous vehicle possible attacks exploits and vulnerabilities"):
  with members:
    - "threat of adversarial attacks on deep learning in computer vision a survey"

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)