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: "deep visual mpc policy learning for navigation"
  Abstract: Humans can routinely follow a trajectory defined by a list of images/landmarks. However, traditional robot navigation methods require accurate mapping of the environment, localization, and planning. Moreover, these methods are sensitive to subtle changes in the environment. In this paper, we propose a Deep Visual MPC-policy learning method that can perform visual navigation while avoiding collisions with unseen objects on the navigation path. Our model PoliNet takes in as input a visual trajectory and the image of the robot's current view and outputs velocity commands for a planning horizon of $N$ steps that optimally balance between trajectory following and obstacle avoidance. PoliNet is trained using a strong image predictive model and traversability estimation model in a MPC setup, with minimal human supervision. Different from prior work, PoliNet can be applied to new scenes without retraining. We show experimentally that the robot can follow a visual trajectory when varying start position and in the presence of previously unseen obstacles. We validated our algorithm with tests both in a realistic simulation environment and in the real world. We also show that we can generate visual trajectories in simulation and execute the corresponding path in the real environment. Our approach outperforms classical approaches as well as previous learning-based baselines in success rate of goal reaching, sub-goal coverage rate, and computational load.

Connected through hyperedge HE-1 (induced by source paper "badgr an autonomous self supervised learning based navigation system"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "neural autonomous navigation with riemannian motion policy"
    - "composable action conditioned predictors flexible off policy learning for robot navigation"
    - "deep ordinal regression network for monocular depth estimation"
    - "semi parametric topological memory for navigation"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "learning to drive in a day"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-2 (induced by source paper "interactive gibson benchmark for interactive navigation in cluttered environments"):
  with members:
    - "doorgym a scalable door opening environment and baseline agent"
    - "vrkitchen an interactive 3d virtual environment for task oriented learning"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "habitat a platform for embodied ai research"
    - "gibson env real world perception for embodied agents"
    - "unity a general platform for intelligent agents"
    - "on evaluation of embodied navigation agents"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "scaling local control to large scale topological navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "bayesian relational memory for semantic visual navigation"
    - "neural autonomous navigation with riemannian motion policy"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"

Connected through hyperedge HE-4 (induced by source paper "effects of a social force model reward in robot navigation based on deep reinforcement learning"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "long range indoor navigation with prm rl"
    - "reward shaping via meta learning"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep 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)