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 novel plsa based traffic signs classification system"
  Abstract: In this work we developed a novel and fast traffic sign recognition system, a very important part for advanced driver assistance system and for autonomous driving. Traffic signs play a very vital role in safe driving and avoiding accident. We have used image processing and topic discovery model pLSA to tackle this challenging multiclass classification problem. Our algorithm is consist of two parts, shape classification and sign classification for improved accuracy. For processing and representation of image we have used bag of features model with SIFT local descriptor. Where a visual vocabulary of size 300 words are formed using k-means codebook formation algorithm. We exploited the concept that every image is a collection of visual topics and images having same topics will belong to same category. Our algorithm is tested on German traffic sign recognition benchmark (GTSRB) and gives very promising result near to existing state of the art techniques.

Connected through hyperedge HE-1 (induced by source paper "deep learning for large scale traffic sign detection and recognition"):
  with members:
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "traffic sign classification using deep inception based convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "total recall understanding traffic signs using deep hierarchical convolutional neural networks"):
  with members:
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "perceptual generative adversarial networks for small object detection"):
  with members:
    - "how far are we from solving pedestrian detection"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "is faster r cnn doing well for pedestrian detection"
    - "ssd single shot multibox detector"
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "traffic sign classification using deep inception based convolutional networks"):
  with members:
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "vehicle local position estimation system"
    - "a robust lane detection and departure warning system"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "characterizing driving behavior using automatic visual analysis"

Connected through hyperedge HE-5 (induced by source paper "a robust lane detection and departure warning system"):
  with members:
    - "characterizing driving behavior using automatic visual analysis"
    - "real time detection of lane markers in urban streets"

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)