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 data driven model for interaction aware pedestrian motion prediction in object cluttered environments"
  Abstract: This paper reports on a data-driven, interaction-aware motion prediction approach for pedestrians in environments cluttered with static obstacles. When navigating in such workspaces shared with humans, robots need accurate motion predictions of the surrounding pedestrians. Human navigation behavior is mostly influenced by their surrounding pedestrians and by the static obstacles in their vicinity. In this paper we introduce a new model based on Long-Short Term Memory (LSTM) neural networks, which is able to learn human motion behavior from demonstrated data. To the best of our knowledge, this is the first approach using LSTMs, that incorporates both static obstacles and surrounding pedestrians for trajectory forecasting. As part of the model, we introduce a new way of encoding surrounding pedestrians based on a 1d-grid in polar angle space. We evaluate the benefit of interaction-aware motion prediction and the added value of incorporating static obstacles on both simulation and real-world datasets by comparing with state-of-the-art approaches. The results show, that our new approach outperforms the other approaches while being very computationally efficient and that taking into account static obstacles for motion predictions significantly improves the prediction accuracy, especially in cluttered environments.

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"
    - "context aware pedestrian motion prediction in urban intersections"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning modular representations for long term multi agent motion predictions"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "learning latent dynamics for planning from pixels"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "real time prediction of intermediate horizon automotive collision risk"
    - "recurrent world models facilitate policy evolution"
    - "desire distant future prediction in dynamic scenes with interacting agents"

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"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "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"

Connected through hyperedge HE-4 (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"
    - "context aware pedestrian motion prediction in urban intersections"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "social ways learning multi modal distributions of pedestrian trajectories with gans"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "social attention modeling attention in human crowds"
    - "unrolled generative adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-6 (induced by source paper "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"):
  with members:
    - "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"

Connected through hyperedge HE-7 (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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "dynamic objects segmentation for visual localization in urban environments"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "carla an open urban driving simulator"
    - "mask r cnn"
    - "modeling cooperative navigation in dense human crowds"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "fast r cnn"

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.RO(Robotics)