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: "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
  Abstract: Predicting the collective motion of a group of pedestrians (a crowd) under the vehicle influence is essential for the development of autonomous vehicles to deal with mixed urban scenarios where interpersonal interaction and vehicle-crowd interaction (VCI) are significant. This usually requires a model that can describe individual pedestrian motion under the influence of nearby pedestrians and the vehicle. This study proposed two pedestrian trajectory datasets, CITR dataset and DUT dataset, so that the pedestrian motion models can be further calibrated and verified, especially when vehicle influence on pedestrians plays an important role. CITR dataset consists of experimentally designed fundamental VCI scenarios (front, back, and lateral VCIs) and provides unique ID for each pedestrian, which is suitable for exploring a specific aspect of VCI. DUT dataset gives two ordinary and natural VCI scenarios in crowded university campus, which can be used for more general purpose VCI exploration. The trajectories of pedestrians, as well as vehicles, were extracted by processing video frames that come from a down-facing camera mounted on a hovering drone as the recording equipment. The final trajectories of pedestrians and vehicles were refined by Kalman filters with linear point-mass model and nonlinear bicycle model, respectively, in which xy-velocity of pedestrians and longitudinal speed and orientation of vehicles were estimated. The statistics of the velocity magnitude distribution demonstrated the validity of the proposed dataset. In total, there are approximate 340 pedestrian trajectories in CITR dataset and 1793 pedestrian trajectories in DUT dataset. The dataset is available at GitHub.

Connected through hyperedge HE-1 (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"
    - "control of connected and automated vehicles state of the art and future challenges"

Connected through hyperedge HE-2 (induced by source paper "path planning in dynamic environments using generative rnns and monte carlo tree search"):
  with members:
    - "human motion trajectory prediction a survey"
    - "predicting responses to a robot s future motion using generative recurrent neural networks"
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "social attention modeling attention in human crowds"

Connected through hyperedge HE-3 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "human motion trajectory prediction a survey"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "an lstm network for highway trajectory prediction"
    - "deep imitative models for flexible inference planning and control"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "hg dagger interactive imitation learning with human experts"

Connected through hyperedge HE-4 (induced by source paper "the ind dataset a drone dataset of naturalistic road user trajectories at german intersections"):
  with members:
    - "intentions of vulnerable road users detection and forecasting by means of machine learning"
    - "beziergan automatic generation of smooth curves from interpretable low dimensional parameters"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
    - "ssd single shot multibox detector"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"

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)