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: "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
  Abstract: We present a novel algorithm for computing collision-free navigation for heterogeneous road-agents such as cars, tricycles, bicycles, and pedestrians in dense traffic. Our approach currently assumes the positions, shapes, and velocities of all vehicles and pedestrians are known and computes smooth trajectories for each agent by taking into account the dynamic constraints. We describe an efficient optimization-based algorithm for each road-agent based on reciprocal velocity obstacles that takes into account kinematic and dynamic constraints. Our algorithm uses tight fitting shape representations based on medial axis to compute collision-free trajectories in dense traffic situations. We evaluate the performance of our algorithm in real-world dense traffic scenarios and highlight the benefits over prior reciprocal collision avoidance schemes.

Connected through hyperedge HE-1 (induced by source paper "inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi agent reinforcement learning"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "learning agile and dynamic motor skills for legged robots"
    - "enabling intuitive human robot teaming using augmented reality and gesture control"
    - "cleaning tasks knowledge transfer between heterogeneous robots a deep learning approach"
    - "zero shot autonomous vehicle policy transfer from simulation to real world via adversarial learning"
    - "a comprehensive review of shepherding as a bio inspired swarm robotics guidance approach"
    - "learning attentional communication for multi agent cooperation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "object detection with deep learning a review"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "roadtrack realtime tracking of road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "a hybrid data association framework for robust online multi object tracking"

Connected through hyperedge HE-4 (induced by source paper "roadtrack tracking road agents in dense and heterogeneous environments"):
  with members:
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "a hybrid data association framework for robust online multi object tracking"
    - "simple online and realtime tracking with a deep association metric"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"

Connected through hyperedge HE-5 (induced by source paper "personality aware probabilistic map for trajectory prediction of pedestrians"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "towards binary valued gates for robust lstm training"
    - "an lstm network for highway trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "crowd behavior simulation with emotional contagion in unexpected multi hazard situations"
    - "context aware 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"

Connected through hyperedge HE-6 (induced by source paper "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "identifying driver behaviors using trajectory features for vehicle navigation"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "object detection with deep learning a review"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-7 (induced by source paper "trafficpredict trajectory prediction for heterogeneous traffic agents"):
  with members:
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "identifying driver behaviors using trajectory features for vehicle navigation"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "identifying driver behaviors using trajectory features for vehicle navigation"):
  with members:
    - "autonomous driving among many pedestrians models and algorithms"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"
    - "enabling robots to communicate their objectives"
    - "end to end learning for self driving cars"
    - "lcrowdv generating labeled videos for simulation based crowd behavior 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)