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: "discrete and continuous probabilistic anticipation for autonomous robots in urban environments"
  Abstract: This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to their ability to probabilistically capture continuous and discrete obstacle decisions and behaviors; the predictive system uses the probabilistic output (state estimate and covariance) of a tracking system, and map of the environment to compute the probability distribution over future obstacle states for a specified anticipation horizon. A Gaussian splitting method is proposed based on the sigma-point transform and the nonlinear dynamics function, which enables increased accuracy as the number of mixands grows. An approach to caching elements of this optimal splitting method is proposed, in order to enable real-time implementation. Simulation results and evaluations on data from the research community demonstrate that the proposed algorithm can accurately anticipate the probability distributions over future states of nonlinear systems.

Connected through hyperedge HE-1 (induced by source paper "uncertainty aware driver trajectory prediction at urban intersections"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "variational end to end navigation and localization"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "autorally an open platform for aggressive autonomous driving"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"):
  with members:
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "carla an open urban driving simulator"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "multi vehicle interaction scenarios generation with interpretable traffic primitives and gaussian process regression"):
  with members:
    - "clustering of driving scenarios using connected vehicle datasets"
    - "understanding v2v driving scenarios through traffic primitives"
    - "end to end learning for self driving cars"
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-4 (induced by source paper "foresee attentive future projections of chaotic road environments with online training"):
  with members:
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "yolo9000 better faster stronger"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "deep residual learning for image recognition"
    - "feed forward networks with attention can solve some long term memory problems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "understanding v2v driving scenarios through traffic primitives"):
  with members:
    - "learning and inferring a driver s braking action in car following scenarios"
    - "clustering of driving scenarios using connected vehicle datasets"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-6 (induced by source paper "a survey of motion planning and control techniques for self driving urban vehicles"):
  with members:
    - "route planning in transportation networks"
    - "optimal sampling based motion planning under differential constraints the driftless case"
    - "sampling based algorithms for optimal motion planning"
    - "theta any angle path planning on grids"
    - "anytime heuristic search"

Connected through hyperedge HE-7 (induced by source paper "computation of forward stochastic reach sets application to stochastic dynamic obstacle avoidance"):
  with members:
    - "safe platooning of unmanned aerial vehicles via reachability"
    - "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)