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: "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
  Abstract: Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reasonable effort, contain naturalistic behavior of road users and include all data relevant for a description of the identified scenarios in sufficient quality. However, the current measurement methods fail to meet at least one of the requirements. Thus, we propose a novel method to measure data from an aerial perspective for scenario-based validation fulfilling the mentioned requirements. Furthermore, we provide a large-scale naturalistic vehicle trajectory dataset from German highways called highD. We evaluate the data in terms of quantity, variety and contained scenarios. Our dataset consists of 16.5 hours of measurements from six locations with 110 000 vehicles, a total driven distance of 45 000 km and 5600 recorded complete lane changes. The highD dataset is available online at: this http URL

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "overview of tools supporting planning for automated driving"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "self driving cars a survey"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "design space of behaviour planning for autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"

Connected through hyperedge HE-3 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding"):
  with members:
    - "human motion trajectory prediction a survey"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "au air a multi modal unmanned aerial vehicle dataset for low altitude traffic surveillance"):
  with members:
    - "autonomous aerial cinematography in unstructured environments with learned artistic decision making"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "vision meets drones a challenge"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-5 (induced by source paper "scale net scalable vehicle trajectory prediction network under random number of interacting vehicles via edge enhanced graph convolutional neural network"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "exploiting edge features in graph neural networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-6 (induced by source paper "spatiotemporal learning of multivehicle interaction patterns in lane change scenarios"):
  with members:
    - "incremental learning of motion primitives for pedestrian trajectory prediction at intersections"
    - "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"
    - "recurrent attentive neural process for sequential data"
    - "a general framework of learning multi vehicle interaction patterns from videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-7 (induced by source paper "causal transfer for imitation learning and decision making under sensor shift"):
  with members:
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "causal confusion in imitation learning"
    - "generative adversarial imitation learning"
    - "experimental and causal view on information integration in autonomous agents"
    - "counterfactual probabilities computational methods bounds and applications"

Connected through hyperedge HE-8 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "scene induced multi modal trajectory forecasting via planning"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "preventing posterior collapse with delta vaes"
    - "structured output learning with conditional generative flows"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "videoflow a flow based generative model for video"

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.CV(Computer Vision and Pattern Recognition)