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: "capsule neural network based height classification using low cost automotive ultrasonic sensors"
  Abstract: High performance ultrasonic sensor hardware is mainly used in medical applications. Although, the development in automotive scenarios is towards autonomous driving, the ultrasonic sensor hardware still stays low-cost and low-performance, respectively. To overcome the strict hardware limitations, we propose to use capsule neural networks. By the high classification capability of this network architecture, we can achieve outstanding results for performing a detailed height analysis of detected objects. We apply a novel resorting and reshaping method to feed the neural network with ultrasonic data. This increases classification performance and computation speed. We tested the approach under different environmental conditions to verify that the proposed method is working independent of external parameters that is needed for autonomous driving.

Connected through hyperedge HE-1 (induced by source paper "capsule network performance with autonomous navigation"):
  with members:
    - "advanced capsule networks via context awareness"
    - "deep reinforcement learning"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "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-2 (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"
    - "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"
    - "novel deep learning model for traffic sign detection using capsule networks"

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)