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: "extracting traffic primitives directly from naturalistically logged data for self driving applications"
  Abstract: Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from high-dimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this letter has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that, we introduce a nonparametric Bayesian learning method—a sticky hierarchical Dirichlet process hidden Markov model—to automatically extract primitives from multidimensional traffic data without prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal learning of multivehicle interaction patterns in lane change scenarios"):
  with members:
    - "incremental learning of motion primitives for pedestrian trajectory prediction at intersections"
    - "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"
    - "recurrent attentive neural process for sequential data"
    - "a general framework of learning multi vehicle interaction patterns from videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-2 (induced by source paper "modeling multi vehicle interaction scenarios using gaussian random field"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "interaction aware probabilistic behavior prediction in urban environments"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"

Connected through hyperedge HE-3 (induced by source paper "how to evaluate proving grounds for self driving a quantitative approach"):
  with members:
    - "clustering of driving encounter scenarios using connected vehicle trajectories"
    - "understanding v2v driving scenarios through traffic primitives"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-4 (induced by source paper "how to evaluate self driving testing ground a quantitative approach"):
  with members:
    - "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"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-5 (induced by source paper "estimating risk levels of driving scenarios through analysis of driving styles for autonomous vehicles"):
  with members:
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-6 (induced by source paper "hierarchical reinforcement learning framework towards multi agent navigation"):
  with members:
    - "learning and inferring a driver s braking action in car following scenarios"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "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-7 (induced by source paper "a new multi vehicle trajectory generator to simulate vehicle to vehicle encounters"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "improved training of wasserstein gans"
    - "a neural representation of sketch drawings"
    - "incremental parsing with minimal features using bi directional lstm"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "cluster naturalistic driving encounters using deep unsupervised learning"):
  with members:
    - "autoencoder regularized network for driving style representation learning"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "trafficnet an open naturalistic driving scenario library"
    - "empirical study of dsrc performance based on safety pilot model deployment data"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

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)