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: "watch this scalable cost function learning for path planning in urban environments"
  Abstract: In this work, we present an approach to learn cost maps for driving in complex urban environments from a very large number of demonstrations of driving behaviour by human experts. The learned cost maps are constructed directly from raw sensor measurements, bypassing the effort of manually designing cost maps as well as features. When deploying the learned cost maps, the trajectories generated not only replicate human-like driving behaviour but are also demonstrably robust against systematic errors in putative robot configuration. To achieve this we deploy a Maximum Entropy based, non-linear IRL framework which uses Fully Convolutional Neural Networks (FCNs) to represent the cost model underlying expert driving behaviour. Using a deep, parametric approach enables us to scale efficiently to large datasets and complex behaviours by being run-time independent of dataset extent during deployment. We demonstrate the scalability and the performance of the proposed approach on an ambitious dataset collected over the course of one year including more than 25k demonstration trajectories extracted from over 120km of driving around pedestrianised areas in the city of Milton Keynes, UK. We evaluate the resulting cost representations by showing the advantages over a carefully manually designed cost map and, in addition, demonstrate its robustness to systematic errors by learning precise cost-maps even in the presence of system calibration perturbations.

Connected through hyperedge HE-1 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-2 (induced by source paper "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "path integral networks end to end differentiable optimal control"
    - "memory augmented control networks"
    - "qmdp net deep learning for planning under partial observability"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "cognitive mapping and planning for visual navigation"
    - "value iteration networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving"):
  with members:
    - "when humans aren t optimal robots that collaborate with risk aware humans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "energy based continuous inverse optimal control"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "belief and truth in hypothesised behaviours"
    - "efficient planning under uncertainty with macro actions"

Connected through hyperedge HE-4 (induced by source paper "active task inference guided deep inverse reinforcement learning"):
  with members:
    - "taco learning task decomposition via temporal alignment for control"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "maximum entropy deep inverse reinforcement learning"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"

Connected through hyperedge HE-5 (induced by source paper "fully convolutional search heuristic learning for rapid path planners"):
  with members:
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "learning heuristic search via imitation"
    - "cognitive mapping and planning for visual navigation"
    - "potential functions based sampling heuristic for optimal path planning"
    - "cnn architectures for large scale audio classification"
    - "value iteration networks"
    - "multi scale context aggregation by dilated convolutions"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"):
  with members:
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "end to end race driving with deep reinforcement learning"
    - "deep imitative models for flexible inference planning and control"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "scene induced multi modal trajectory forecasting via planning"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"

Connected through hyperedge HE-8 (induced by source paper "off road autonomous vehicles traversability analysis and trajectory planning based on deep inverse reinforcement learning"):
  with members:
    - "off road drivable area extraction using 3d lidar data"
    - "an algorithmic perspective on imitation learning"
    - "integrating kinematics and environment context into deep inverse reinforcement learning for predicting off road vehicle trajectories"
    - "end to end learning for self driving cars"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "value iteration networks"
    - "maximum entropy deep inverse reinforcement learning"

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)