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: "context aware pedestrian motion prediction in urban intersections"
  Abstract: This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-based world and subsequently predict pedestrian trajectories by modeling the transition between learned primitives as a Gaussian Process (GP). This work extends that prior approach by incorporating semantic features from the environment (relative distance to curbside and status of pedestrian traffic lights) in the GP formulation for more accurate predictions of pedestrian trajectories over the same timescale. We evaluate the new approach on real-world data collected using one of the vehicles in the MIT Mobility On Demand fleet. The results show 12.5% improvement in prediction accuracy and a 2.65 times reduction in Area Under the Curve (AUC), which is used as a metric to quantify the span of predicted set of trajectories, such that a lower AUC corresponds to a higher level of confidence in the future direction of pedestrian motion.

Connected through hyperedge HE-1 (induced by source paper "predicting motion of vulnerable road users using high definition maps and efficient convnets"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "scene lstm a model for human trajectory prediction"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-2 (induced by source paper "what the constant velocity model can teach us about pedestrian motion prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"
    - "context aware trajectory prediction"

Connected through hyperedge HE-3 (induced by source paper "deep context map agent trajectory prediction using location specific latent maps"):
  with members:
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "car net clairvoyant attentive recurrent network"
    - "pedestrian prediction by planning using deep neural networks"
    - "a data driven model for interaction aware pedestrian motion prediction in object cluttered environments"

Connected through hyperedge HE-4 (induced by source paper "multimodal interaction aware motion prediction for autonomous street crossing"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "scene lstm a model for human trajectory prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "why did the robot cross the road learning from multi modal sensor data for autonomous road crossing"

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)