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 vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
  Abstract: Accurate and robust tracking of surrounding road participants plays an important role in autonomous driving. However, there is usually no prior knowledge of the number of tracking targets due to object emergence, object disappearance and false alarms. To overcome this challenge, we propose a generic vehicle tracking framework based on modified mixture particle filter, which can make the number of tracking targets adaptive to real-time observations and track all the vehicles within sensor range simultaneously in a uniform architecture without explicit data association. Each object corresponds to a mixture component whose distribution is non-parametric and approximated by particle hypotheses. Most tracking approaches employ vehicle kinematic models as the prediction model. However, it is hard for these models to make proper predictions when sensor measurements are lost or become low quality due to partial or complete occlusions. Moreover, these models are incapable of forecasting sudden maneuvers. To address these problems, we propose to incorporate learning-based behavioral models instead of pure vehicle kinematic models to realize prediction in the prior update of recursive Bayesian state estimation. Two typical driving scenarios including lane keeping and lane change are demonstrated to verify the effectiveness and accuracy of the proposed framework as well as the advantages of employing learning-based models.

Connected through hyperedge HE-1 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "social and scene aware trajectory prediction in crowded spaces"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "diverse trajectory forecasting with determinantal point processes"
    - "multiple futures prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"

Connected through hyperedge HE-2 (induced by source paper "tppo a novel trajectory predictor with pseudo oracle"):
  with members:
    - "social and scene aware trajectory prediction in crowded spaces"
    - "scene induced multi modal trajectory forecasting via planning"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "safecritic collision aware trajectory prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"

Connected through hyperedge HE-3 (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"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "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-4 (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 probabilistic interactive situation recognition and prediction from virtual to real"
    - "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-5 (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"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"

Connected through hyperedge HE-6 (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"
    - "social cohesion in autonomous driving"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-7 (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"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "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-8 (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 probabilistic interactive situation recognition and prediction from virtual to real"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"
    - "bayesian conditional generative adverserial networks"
    - "conditional generative adversarial nets"

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)