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: "markov localization for mobile robots in dynamic environments"
  Abstract: Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.

Connected through hyperedge HE-1 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "multi attention recurrent network for human communication comprehension"
    - "map management for efficient long term visual localization in outdoor environments"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "neural autonomous navigation with riemannian motion policy"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "riemannian motion policies"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "end to end driving via conditional imitation learning"
    - "sfm net learning of structure and motion from video"
    - "cognitive mapping and planning for visual navigation"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-3 (induced by source paper "deep active localization"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "active neural localization"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "proximal policy optimization algorithms"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "detection by localization maintenance free change object detector"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "yolov3 an incremental improvement"
    - "map management for efficient long term visual localization in outdoor environments"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "lightweight unsupervised deep loop closure"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "learning deep structured network for weakly supervised change detection"

Connected through hyperedge HE-5 (induced by source paper "getting robots unfrozen and unlost in dense pedestrian crowds"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "scale robust localization using general object landmarks"):
  with members:
    - "visual place recognition using landmark distribution descriptors"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "deep residual learning for image recognition"
    - "orb slam a versatile and accurate monocular slam system"
    - "on the performance of convnet features for place recognition"

Connected through hyperedge HE-7 (induced by source paper "an opportunistic linear convex algorithm for localization in mobile robot networks"):
  with members:
    - "localization in mobile networks via virtual convex hulls"
    - "asymptotic stability of stochastic ltv systems with applications to distributed dynamic fusion"

Connected through hyperedge HE-8 (induced by source paper "betarun 2017 team description paper variety complexity and learning"):
  with members:
    - "deep learning in neural 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.AI(Artificial Intelligence)