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: "a transferable pedestrian motion prediction model for intersections with different geometries"
  Abstract: This paper presents a novel framework for accurate pedestrian intent prediction at intersections. Given some prior knowledge of the curbside geometry, the presented framework can accurately predict pedestrian trajectories, even in new intersections that it has not been trained on. This is achieved by making use of the contravariant components of trajectories in the curbside coordinate system, which ensures that the transformation of trajectories across intersections is affine, regardless of the curbside geometry. Our method is based on the Augmented Semi Nonnegative Sparse Coding (ASNSC) formulation and we use that as a baseline to show improvement in prediction performance on real pedestrian datasets collected at two intersections in Cambridge, with distinctly different curbside and crosswalk geometries. We demonstrate a 7.2% improvement in prediction accuracy in the case of same train and test intersections. Furthermore, we show a comparable prediction performance of TASNSC when trained and tested in different intersections with the baseline, trained and tested on the same intersection.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"

Connected through hyperedge HE-2 (induced by source paper "peeking into the future predicting future person activities and locations in videos"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "scene lstm a model for human trajectory prediction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "relation networks for object detection"
    - "future person localization in first person videos"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-3 (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"
    - "context aware pedestrian motion prediction in urban intersections"
    - "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-4 (induced by source paper "incremental learning of motion primitives for pedestrian trajectory prediction at intersections"):
  with members:
    - "transferable pedestrian motion prediction models at intersections"
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"
    - "real time predictive modeling and robust avoidance of pedestrians with uncertain changing intentions"

Connected through hyperedge HE-5 (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"
    - "context aware pedestrian motion prediction in urban intersections"
    - "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)