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: "sampling based algorithms for optimal motion planning using closed loop prediction"
  Abstract: Motion planning under differential constraints, kinodynamic motion planning, is one of the canonical problems in robotics. Currently, state-of-the-art methods evolve around kinodynamic variants of popular sampling-based algorithms, such as Rapidly-exploring Random Trees (RRTs). However, there are still challenges remaining, for example, how to include complex dynamics while guaranteeing optimality. If the open-loop dynamics are unstable, exploration by random sampling in control space becomes inefficient. We describe a new sampling-based algorithm, called CL-RRT#, which leverages ideas from the RRT# algorithm and a variant of the RRT algorithm that generates trajectories using closed-loop prediction. The idea of planning with closed-loop prediction allows us to handle complex unstable dynamics and avoids the need to find computationally hard steering procedures. The search technique presented in the RRT# algorithm allows us to improve the solution quality by searching over alternative reference trajectories. Numerical simulations using a nonholonomic system demonstrate the benefits of the proposed approach.

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"
    - "redundant perception and state estimation for reliable autonomous racing"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-2 (induced by source paper "real time multi target path prediction and planning for autonomous driving aided by fcn"):
  with members:
    - "learning lightweight lane detection cnns by self attention distillation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "tiev the tongji intelligent electric vehicle in the intelligent vehicle future challenge of china"
    - "dual attention network for scene segmentation"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "pyramid scene parsing network"

Connected through hyperedge HE-3 (induced by source paper "path planning using positive invariant sets"):
  with members:
    - "sampling based algorithms for optimal motion planning"

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)