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: "autonomous driving among many pedestrians models and algorithms"
  Abstract: Driving among a dense crowd of pedestrians is a major challenge for autonomous vehicles. This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time. Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.

Connected through hyperedge HE-1 (induced by source paper "identifying driver behaviors using trajectory features for vehicle navigation"):
  with members:
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"
    - "enabling robots to communicate their objectives"
    - "end to end learning for self driving cars"
    - "lcrowdv generating labeled videos for simulation based crowd behavior learning"

Connected through hyperedge HE-2 (induced by source paper "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"):
  with members:
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "identifying driver behaviors using trajectory features for vehicle navigation"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "carla an open urban driving simulator"

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)