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: "pedestrian prediction by planning using deep neural networks"
  Abstract: Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately.

Connected through hyperedge HE-1 (induced by source paper "extending maps with semantic and contextual object information for robot navigation a learning based framework using visual and depth cues"):
  with members:
    - "kaolin a pytorch library for accelerating 3d deep learning research"
    - "yolact real time instance segmentation"
    - "mapping road lanes using laser remission and deep neural networks"
    - "end to end recovery of human shape and pose"
    - "mix and match tuning for self supervised semantic segmentation"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "mask r cnn"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-2 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction"):
  with members:
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"

Connected through hyperedge HE-3 (induced by source paper "scene compliant trajectory forecast with agent centric spatio temporal grids"):
  with members:
    - "scene induced multi modal trajectory forecasting via planning"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "human motion trajectory prediction a survey"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "social attention for autonomous decision making in dense traffic"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "deep imitative models for flexible inference planning and control"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "learning to drive in a day"
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-5 (induced by source paper "discrete residual flow for probabilistic pedestrian behavior prediction"):
  with members:
    - "human motion trajectory prediction a survey"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "car net clairvoyant attentive recurrent network"

Connected through hyperedge HE-7 (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"
    - "a data driven model for interaction aware pedestrian motion prediction in object cluttered environments"

Connected through hyperedge HE-8 (induced by source paper "a hierarchical mixture density network"):
  with members:
    - "density estimation for geolocation via convolutional mixture density network"
    - "recurrent mixture density network for spatiotemporal visual attention"

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)