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: "tackling occlusions limited sensor range with set based safety verification"
  Abstract: Provable safety is one of the most critical challenges in automated driving. The behavior of numerous traffic participants in a scene cannot be predicted reliably due to complex interdependencies and the indiscriminate behavior of humans. Additionally, we face high uncertainties and only incomplete environment knowledge. Recent approaches minimize risk with probabilistic and machine learning methods - even under occlusions. These generate comfortable behavior with good traffic flow, but cannot guarantee safety of their maneuvers. Therefore, we contribute a safety verification method for trajectories under occlusions. The field-of-view of the ego vehicle and a map are used to identify critical sensing field edges, each representing a potentially hidden obstacle. The state of occluded obstacles is unknown, but can be over-approximated by intervals over all possible states. Then set-based methods are extended to provide occupancy predictions for obstacles with state intervals. The proposed method can verify the safety of given trajectories (e.g. if they ensure collision-free fail-safe maneuver options) w.r.t. arbitrary safe-state formulations. The potential for provably safe trajectory planning is shown in three evaluative scenarios.

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"
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "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"
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "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 "autonomous vehicles meet the physical world rss variability uncertainty and proving safety expanded version"):
  with members:
    - "towards a framework to manage perceptual uncertainty for safe automated driving"
    - "ontology based scene creation for the development of automated vehicles"
    - "on a formal model of safe and scalable self driving cars"
    - "a sequential two step algorithm for fast generation of vehicle racing trajectories"
    - "visualizing deep convolutional neural networks using natural pre images"

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"
    - "occlusion aware risk assessment for autonomous driving in urban environments"

Connected through hyperedge HE-5 (induced by source paper "pedestrian collision avoidance system for scenarios with occlusions"):
  with members:
    - "evaluation of automated vehicles encountering pedestrians at unsignalized crossings"

Connected through hyperedge HE-6 (induced by source paper "occlusion aware risk assessment for autonomous driving in urban environments"):
  with members:
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "ssd single shot multibox detector"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)