human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"):
  Abstract: Behavior-related research areas such as motion prediction/planning, representation/imitation learning, behavior modeling/generation, and algorithm testing, require support from high-quality motion datasets containing interactive driving scenarios with different driving cultures. In this paper, we present an INTERnational, Adversarial and Cooperative moTION dataset (INTERACTION dataset) in interactive driving scenarios with semantic maps. Five features of the dataset are highlighted. 1) The interactive driving scenarios are diverse, including urban/highway/ramp merging and lane changes, roundabouts with yield/stop signs, signalized intersections, intersections with one/two/all-way stops, etc. 2) Motion data from different countries and different continents are collected so that driving preferences and styles in different cultures are naturally included. 3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants. Highly complex behavior such as negotiations, aggressive/irrational decisions and traffic rule violations are densely contained in the dataset, while regular behavior can also be found from cautious car-following, stop, left/right/U-turn to rational lane-change and cycling and pedestrian crossing, etc. 4) The levels of criticality span wide, from regular safe operations to dangerous, near-collision maneuvers. Real collision, although relatively slight, is also included. 5) Maps with complete semantic information are provided with physical layers, reference lines, lanelet connections and traffic rules. The data is recorded from drones and traffic cameras. Statistics of the dataset in terms of number of entities and interaction density are also provided, along with some utilization examples in a variety of behavior-related research areas. The dataset can be downloaded via this https URL.

Includes members:
  Member "generic prediction architecture considering both rational and irrational driving behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory")
  Member "human motion trajectory prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points")
  Member "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generic prediction architecture considering both rational and irrational driving behaviors")
  Member "an lstm network for highway trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "deep imitative models for flexible inference planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "discriminative correlation filter tracker with channel and spatial reliability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tracking of micro unmanned aerial vehicles a comparative study")
  Member "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")

Question: Please tell me which class the source paper 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)