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: "learning and inferring a driver s braking action in car following scenarios"
  Abstract: Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper, we focus on predicting a driver's intent to brake in car-following scenarios from a perception–decision–action perspective according to his/her driving history. A learning-based inference method, using onboard data from CAN-Bus, radar, and cameras as explanatory variables, is introduced to infer drivers’ braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM). The GMM is used to model stochastic relationships among variables, while the HMM is applied to infer drivers’ braking actions based on the GMM. Real-case driving data from 49 drivers (more than three years’ driving data per driver on average) have been collected from the University of Michigan Safety Pilot Model Deployment database. We compare the GMM-HMM method to a support vector machine (SVM) method and a SVM-Bayesian filtering method. The experimental results are evaluated by employing three performance metrics:  accuracy ,  sensitivity , and  specificity . The comparison results show that the GMM–HMM obtains the best performance, with an accuracy of 90%, sensitivity of 84%, and specificity of 97%. Thus, we believe that this method has great potential for real-world active safety systems.

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 "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-3 (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-4 (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"
    - "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-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"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "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 "how shall i drive interaction modeling and motion planning towards empathetic and socially graceful driving"):
  with members:
    - "courteous autonomous cars"
    - "learning with opponent learning awareness"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

Connected through hyperedge HE-7 (induced by source paper "hierarchical reinforcement learning framework towards multi agent navigation"):
  with members:
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "hindsight experience replay"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"

Connected through hyperedge HE-8 (induced by source paper "understanding v2v driving scenarios through traffic primitives"):
  with members:
    - "clustering of driving scenarios using connected vehicle datasets"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
    - "discrete and continuous probabilistic anticipation for autonomous robots in urban environments"

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)