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: "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
  Abstract: Autonomous vehicles (AVs) are on the road. To safely and efficiently interact with other road participants, AVs have to accurately predict the behavior of surrounding vehicles and plan accordingly. Such prediction should be probabilistic, to address the uncertainties in human behavior. Such prediction should also be interactive, since the distribution over all possible trajectories of the predicted vehicle depends not only on historical information, but also on future plans of other vehicles that interact with it. To achieve such interaction-aware predictions, we propose a probabilistic prediction approach based on hierarchical inverse reinforcement learning (IRL). First, we explicitly consider the hierarchical trajectory-generation process of human drivers involving both discrete and continuous driving decisions. Based on this, the distribution over all future trajectories of the predicted vehicle is formulated as a mixture of distributions partitioned by the discrete decisions. Then we apply IRL hierarchically to learn the distributions from real human demonstrations. A case study for the ramp-merging driving scenario is provided. The quantitative results show that the proposed approach can accurately predict both the discrete driving decisions such as yield or pass as well as the continuous trajectories.

Connected through hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "potential field interpretable and unified representation for trajectory prediction"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"

Connected through hyperedge HE-2 (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"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "driving with style inverse reinforcement learning in general purpose planning for automated driving"
    - "agil learning attention from human for visuomotor tasks"
    - "the effect of whole body haptic feedback on driver s perception in negotiating a curve"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "learning to drive using inverse reinforcement learning and deep q networks"

Connected through hyperedge HE-4 (induced by source paper "multi modal probabilistic prediction of interactive behavior via an interpretable model"):
  with members:
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "courteous autonomous cars"

Connected through hyperedge HE-5 (induced by source paper "generic prediction architecture considering both rational and irrational driving behaviors"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "a framework for probabilistic generic traffic scene prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "courteous autonomous cars"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-6 (induced by source paper "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "a framework for probabilistic generic traffic scene prediction"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "courteous autonomous cars"

Connected through hyperedge HE-7 (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"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "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"
    - "courteous autonomous cars"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-8 (induced by source paper "towards better human robot collaboration with robust plan recognition and trajectory prediction"):
  with members:
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "human motion prediction using semi adaptable neural networks"
    - "human activity recognition in rgb d videos by dynamic images"
    - "goal inference improves objective and perceived performance in human robot collaboration"
    - "robot safe interaction system for intelligent industrial co robots"
    - "courteous autonomous cars"
    - "learning human motion models for long term predictions"

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.LG(Machine Learning)