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: "a behavioral approach to visual navigation with graph localization networks"
  Abstract: Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps. Our goal is to enable a robot to navigate from one location to another, relying only on its visual input and the topological map of the environment. We propose using graph neural networks for localizing the agent in the map, and decompose the action space into primitive behaviors implemented as convolutional or recurrent neural networks. Using the Gibson simulator, we verify that our approach outperforms relevant baselines and is able to navigate in both seen and unseen environments.

Connected through hyperedge HE-1 (induced by source paper "topological mapping for manhattan like repetitive environments"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "deep residual learning for image recognition"
    - "place categorization and semantic mapping on a mobile robot"

Connected through hyperedge HE-2 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"

Connected through hyperedge HE-3 (induced by source paper "combining optimal control and learning for visual navigation in novel environments"):
  with members:
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "neural autonomous navigation with riemannian motion policy"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "deep drone racing learning agile flight in dynamic environments"
    - "learning navigation behaviors end to end with autorl"
    - "semi parametric topological memory for navigation"
    - "variational end to end navigation and localization"
    - "driving policy transfer via modularity and abstraction"

Connected through hyperedge HE-4 (induced by source paper "hrl4in hierarchical reinforcement learning for interactive navigation with mobile manipulators"):
  with members:
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "learning multi level hierarchies with hindsight"

Connected through hyperedge HE-5 (induced by source paper "deep visual mpc policy learning for navigation"):
  with members:
    - "a neural network based model predictive control of three phase inverter with an output lc filter"
    - "learning agile and dynamic motor skills for legged robots"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "vunet dynamic scene view synthesis for traversability estimation using an rgb camera"

Connected through hyperedge HE-6 (induced by source paper "graph attention memory for visual navigation"):
  with members:
    - "learning exploration policies for navigation"
    - "zero shot visual imitation"
    - "semi parametric topological memory for navigation"
    - "reinforcement learning and deep learning based lateral control for autonomous driving"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "learning to navigate in cities without a map"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "unifying map and landmark based representations for visual navigation"

Connected through hyperedge HE-7 (induced by source paper "scaling local control to large scale topological navigation"):
  with members:
    - "deep visual mpc policy learning for navigation"
    - "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-8 (induced by source paper "mapless navigation among dynamics with social safety awareness a reinforcement learning approach from 2d laser scans"):
  with members:
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "learning end to end multimodal sensor policies for autonomous navigation"

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)