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: "porca modeling and planning for autonomous driving among many pedestrians"
  Abstract: This paper presents a planning system for autonomous driving among many pedestrians. A key ingredient of our approach is PORCA, a pedestrian motion prediction model that accounts for both a pedestrian's global navigation intention and local interactions with the vehicle and other pedestrians. Unfortunately, the autonomous vehicle does not know the pedestrian's intention a priori and requires a planning algorithm that hedges against the uncertainty in pedestrian intentions. Our planning system combines a POMDP algorithm with the pedestrian motion model and runs in near real time. Experiments show that it enables a robot vehicle to drive safely, efficiently, and smoothly among a crowd with a density of nearly one person per square meter.

Connected through hyperedge HE-1 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "social stgcnn a social spatio temporal graph convolutional neural network for human trajectory prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "deepgcns can gcns go as deep as cnns"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "stochastic trajectory prediction with social graph network"
    - "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"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "safe optimal control under parametric uncertainties"):
  with members:
    - "safely probabilistically complete real time planning and exploration in unknown environments"
    - "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments"

Connected through hyperedge HE-4 (induced by source paper "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"):
  with members:
    - "pedx benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "densepose dense human pose estimation in the wild"
    - "decomposing motion and content for natural video sequence prediction"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-5 (induced by source paper "stochastic sampling simulation for pedestrian trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "scene lstm a model for human trajectory prediction"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-6 (induced by source paper "gamma a general agent motion prediction model for autonomous driving"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "an lstm network for highway trajectory prediction"

Connected through hyperedge HE-7 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments"):
  with members:
    - "search based optimal motion planning for automated driving"
    - "collaborative planning for mixed autonomy lane merging"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "carla an open urban driving simulator"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"

Connected through hyperedge HE-8 (induced by source paper "lets drive driving in a crowd by learning from tree search"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "gated path planning networks"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "socially aware motion planning with deep reinforcement learning"
    - "despot online pomdp planning with regularization"
    - "value iteration networks"

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)