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: "occlusion aware risk assessment for autonomous driving in urban environments"
  Abstract: Navigating safely in urban environments remains a challenging problem for autonomous vehicles. Occlusion and limited sensor range can pose significant challenges to safely navigate among pedestrians and other vehicles in the environment. Enabling vehicles to quantify the risk posed by unseen regions allows them to anticipate future possibilities, resulting in increased safety and ride comfort. This paper proposes an algorithm that takes advantage of the known road layouts to forecast, quantify, and aggregate risk associated with occlusions and limited sensor range. This allows us to make predictions of risk induced by unobserved vehicles even in heavily occluded urban environments. The risk can then be used either by a low-level planning algorithm to generate better trajectories, or by a high-level one to plan a better route. The proposed algorithm is evaluated on intersection layouts from real-world map data with up to five other vehicles in the scene, and verified to reduce collision rates by 4.8x comparing to a baseline method while improving driving comfort.

Connected through hyperedge HE-1 (induced by source paper "tackling existence probabilities of objects with motion planning for automated urban driving"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "limited visibility and uncertainty aware motion planning for automated driving"
    - "on a formal model of safe and scalable self driving cars"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"

Connected through hyperedge HE-2 (induced by source paper "behavior planning of autonomous cars with social perception"):
  with members:
    - "people as sensors imputing maps from human actions"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "social cohesion in autonomous driving"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-3 (induced by source paper "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"):
  with members:
    - "identifying robust landmarks in feature based maps"
    - "bridging the gap between safety and real time performance in receding horizon trajectory design for mobile robots"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "on a formal model of safe and scalable self driving cars"
    - "safe trajectory synthesis for autonomous driving in unforeseen environments"
    - "funnel libraries for real time robust feedback motion planning"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"

Connected through hyperedge HE-4 (induced by source paper "risk assessment and planning with bidirectional reachability for autonomous driving"):
  with members:
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "towards provably not at fault control of autonomous robots in arbitrary dynamic environments"
    - "guaranteed safe reachability based trajectory design for a high fidelity model of an autonomous passenger vehicle"
    - "tackling occlusions limited sensor range with set based safety verification"

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)