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: "redundant perception and state estimation for reliable autonomous racing"
  Abstract: In autonomous racing, vehicles operate close to the limits of handling and a sensor failure can have critical consequences. To limit the impact of such failures, this paper presents the redundant perception and state estimation approaches developed for an autonomous race car. Redundancy in perception is achieved by estimating the color and position of the track delimiting objects using two sensor modalities independently. Specifically, learning-based approaches are used to generate color and pose estimates, from LiDAR and camera data respectively. The redundant perception inputs are fused by a particle filter based SLAM algorithm that operates in real-time. Velocity is estimated using slip dynamics, with reliability being ensured through a probabilistic failure detection algorithm. The sub-modules are extensively evaluated in real-world racing conditions using the autonomous race car "gotthard driverless", achieving lateral accelerations up to 1.7G and a top speed of 90km/h.

Connected through hyperedge HE-1 (induced by source paper "fast and accurate mapping for autonomous racing"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "tracking multiple moving objects using unscented kalman filtering techniques"
    - "yolov3 an incremental improvement"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "sampling based algorithms for optimal motion planning using closed loop prediction"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-2 (induced by source paper "end to end velocity estimation for autonomous racing"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "vision based high speed driving with a deep dynamic observer"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "adadelta an adaptive learning rate method"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "amz driverless the full autonomous racing system"):
  with members:
    - "real time 3d traffic cone detection for autonomous driving"
    - "autorally an open platform for aggressive autonomous driving"
    - "design of an autonomous racecar perception state estimation and system integration"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "real time control for autonomous racing based on viability theory"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

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.RO(Robotics)