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: "car type recognition with deep neural networks"
  Abstract: In this paper we study automatic recognition of cars of four types: Bus, Truck, Van and Small car. For this problem we consider two data driven frameworks: a deep neural network and a support vector machine using SIFT features. The accuracy of the methods is validated with a database of over 6500 images, and the resulting prediction accuracy is over 97 %. This clearly exceeds the accuracies of earlier studies that use manually engineered feature extraction pipelines.

Connected through hyperedge HE-1 (induced by source paper "intelligent traffic monitoring systems for vehicle classification a survey"):
  with members:
    - "radio based traffic flow detection and vehicle classification for future smart cities"
    - "xgboost a scalable tree boosting system"
    - "a large scale car dataset for fine grained categorization and verification"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "learning task grouping and overlap in multi task learning"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-2 (induced by source paper "classifying logistic vehicles in cities using deep learning"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "a large scale car dataset for fine grained categorization and verification"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "binarized convolutional neural networks for efficient inference on gpus"):
  with members:
    - "espresso efficient forward propagation for bcnns"
    - "decomposeme simplifying convnets for end to end learning"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "speeding up convolutional neural networks with low rank expansions"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "prune dynamic and decidable dataflow for signal processing on heterogeneous platforms"):
  with members:
    - "executing dynamic data rate actor networks on opencl platforms"

Connected through hyperedge HE-5 (induced by source paper "centrog feature technique for vehicle type recognition at day and night times"):
  with members:
    - "image based vehicle classification system"

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)