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: "attention and anticipation in fast visual inertial navigation"
  Abstract: We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.

Connected through hyperedge HE-1 (induced by source paper "learning pose estimation for uav autonomous navigation and landing using visual inertial sensor data"):
  with members:
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "geometric loss functions for camera pose regression with deep learning"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "visual servoing from deep neural networks"
    - "image matching using sift surf brief and orb performance comparison for distorted images"

Connected through hyperedge HE-2 (induced by source paper "a resource aware approach to collaborative loop closure detection with provable performance guarantees"):
  with members:
    - "resource aware algorithms for distributed loop closure detection with provable performance guarantees"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "data efficient decentralized visual slam"
    - "distributed mapping with privacy and communication constraints lightweight algorithms and object based models"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-3 (induced by source paper "estimation with fast landmark selection in robot visual navigation"):
  with members:
    - "space time sampling for network observability"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-4 (induced by source paper "evaluating trajectory collision probability through adaptive importance sampling for safe motion planning"):
  with members:
    - "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "julia a fast dynamic language for technical computing"

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)