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: "ontology based scene creation for the development of automated vehicles"
  Abstract: The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.

Connected through hyperedge HE-1 (induced by source paper "learning modulated loss for rotated object detection"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "reppoints point set representation for object detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "foveabox beyond anchor based object detector"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (induced by source paper "an agency directed approach to test generation for simulation based autonomous vehicle verification"):
  with members:
    - "learning from demonstration in the wild"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "scenarios for development test and validation of automated vehicles"
    - "intelligent agent based stimulation for testing robotic software in human robot interactions"
    - "coverage driven verification an approach to verify code for robots that directly interact with humans"

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"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "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 "from functional to logical scenarios detailing a keyword based scenario description for execution in a simulation environment"):
  with members:
    - "scenarios for development test and validation of automated vehicles"

Connected through hyperedge HE-5 (induced by source paper "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

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.AI(Artificial Intelligence)