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: "generic probabilistic interactive situation recognition and prediction from virtual to real"
  Abstract: Accurate and robust recognition and prediction of traffic situation plays an important role in autonomous driving, which is a prerequisite for risk assessment and effective decision making. Although there exist a lot of works dealing with modeling driver behavior of a single object, it remains a challenge to make predictions for multiple highly interactive agents that react to each other simultaneously. In this work, we propose a generic probabilistic hierarchical recognition and prediction framework which employs a two-layer Hidden Markov Model (TLHMM) to obtain the distribution of potential situations and a learning-based dynamic scene evolution model to sample a group of future trajectories. Instead of predicting motions of a single entity, we propose to get the joint distribution by modeling multiple interactive agents as a whole system. Moreover, due to the decoupling property of the layered structure, our model is suitable for knowledge transfer from simulation to real world applications as well as among different traffic scenarios, which can reduce the computational efforts of training and the demand for a large data amount. A case study of highway ramp merging scenario is demonstrated to verify the effectiveness and accuracy of the proposed framework.

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 "conditional generative neural system for probabilistic trajectory prediction"):
  with members:
    - "generic tracking and probabilistic prediction framework and its application in autonomous driving"
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "the numerics of gans"
    - "imitating driver behavior with generative adversarial 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"
    - "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"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "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 tracking and probabilistic prediction framework and its application in autonomous driving"):
  with members:
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-6 (induced by source paper "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"
    - "bayesian conditional generative adverserial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "a general framework of learning multi vehicle interaction patterns from videos"):
  with members:
    - "yolov3 an incremental improvement"
    - "clustering of driving scenarios using connected vehicle datasets"
    - "understanding v2v driving scenarios through traffic primitives"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-8 (induced by source paper "a framework for probabilistic generic traffic scene prediction"):
  with members:
    - "an lstm network for highway trajectory prediction"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "an uncertain future forecasting from static images using variational autoencoders"

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)