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: "graph neural networks for modelling traffic participant interaction"
  Abstract: By interpreting a traffic scene as a graph of interacting vehicles, we gain a flexible abstract representation which allows us to apply Graph Neural Network (GNN) models for traffic prediction. These naturally take interaction between traffic participants into account while being computationally efficient and providing large model capacity. We evaluate two state-of-the art GNN architectures and introduce several adaptations for our specific scenario. We show that prediction error in scenarios with much interaction decreases by 30% compared to a model that does not take interactions into account. This suggests a graph interpretation of interacting traffic participants is a worthwhile addition to traffic prediction systems.

Connected through hyperedge HE-1 (induced by source paper "scale net scalable vehicle trajectory prediction network under random number of interacting vehicles via edge enhanced graph convolutional neural network"):
  with members:
    - "interaction aware kalman neural networks for trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "exploiting edge features in graph neural networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction"):
  with members:
    - "scene induced multi modal trajectory forecasting via planning"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "preventing posterior collapse with delta vaes"
    - "structured output learning with conditional generative flows"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "videoflow a flow based generative model for video"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"

Connected through hyperedge HE-3 (induced by source paper "deep learning based vehicle behaviour prediction for autonomous driving applications a review"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "anomaly detection in road traffic using visual surveillance a survey"
    - "survey on vision based path prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "dynamic interaction aware scene understanding for reinforcement learning in autonomous driving"):
  with members:
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "dynamic input for deep reinforcement learning in autonomous driving"
    - "fast graph representation learning with pytorch geometric"
    - "graph neural networks a review of methods and applications"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "graph networks as learnable physics engines for inference and control"
    - "deep sets"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-5 (induced by source paper "graph convolution networks for probabilistic modeling of driving acceleration"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "an lstm network for highway trajectory prediction"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "providentia a large scale sensing system for the assistance of autonomous vehicles"):
  with members:
    - "targetless rotational auto calibration of radar and camera for intelligent transportation systems"
    - "yolov3 an incremental improvement"
    - "traffic danger recognition with surveillance cameras without training data"
    - "understanding traffic density from large scale web camera data"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"

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)