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: "using convolutional neural networks in robots with limited computational resources detecting nao robots while playing soccer"
  Abstract: The main goal of this paper is to analyze the general problem of using Convolutional Neural Networks (CNNs) in robots with limited computational capabilities, and to propose general design guidelines for their use. In addition, two different CNN based NAO robot detectors that are able to run in real-time while playing soccer are proposed. One of the detectors is based on the XNOR-Net and the other on the SqueezeNet. Each detector is able to process a robot object-proposal in ~1ms, with an average number of 1.5 proposals per frame obtained by the upper camera of the NAO. The obtained detection rate is ~97%.

Connected through hyperedge HE-1 (induced by source paper "real time object detection and recognition on low compute humanoid robots using deep learning"):
  with members:
    - "yolov3 an incremental improvement"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-2 (induced by source paper "robo robust fully neural object detection for robot soccer"):
  with members:
    - "yolov3 an incremental improvement"
    - "deep learning for semantic segmentation on minimal hardware"
    - "visual mesh real time object detection using constant sample density"
    - "dynamic routing between capsules"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-3 (induced by source paper "xyolo a model for real time object detection in humanoid soccer on low end hardware"):
  with members:
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "playing soccer without colors in the spl a convolutional neural network approach"
    - "deep learning for semantic segmentation on minimal hardware"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ssd single shot multibox detector"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "you only look once unified real time object detection"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "a framework of transfer learning in object detection for embedded systems"):
  with members:
    - "expanding a robot s life low power object recognition via fpga based dcnn deployment"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-5 (induced by source paper "visual mesh real time object detection using constant sample density"):
  with members:
    - "yolov3 an incremental improvement"
    - "self normalizing neural networks"
    - "machine learning for realisticball detection in robocup spl"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-6 (induced by source paper "deep learning for semantic segmentation on minimal hardware"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "playing soccer without colors in the spl a convolutional neural network approach"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)