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: "perception aware path planning"
  Abstract: In this paper, we give a double twist to the problem of planning under uncertainty. State-of-the-art planners seek to minimize the localization uncertainty by only considering the geometric structure of the scene. In this paper, we argue that motion planning for vision-controlled robots should be perception aware in that the robot should also favor texture-rich areas to minimize the localization uncertainty during a goal-reaching task. Thus, we describe how to optimally incorporate the photometric information (i.e., texture) of the scene, in addition to the the geometric one, to compute the uncertainty of vision-based localization during path planning. To avoid the caveats of feature-based localization systems (i.e., dependence on feature type and user-defined thresholds), we use dense, direct methods. This allows us to compute the localization uncertainty directly from the intensity values of every pixel in the image. We also describe how to compute trajectories online, considering also scenarios with no prior knowledge about the map. The proposed framework is general and can easily be adapted to different robotic platforms and scenarios. The effectiveness of our approach is demonstrated with extensive experiments in both simulated and real-world environments using a vision-controlled micro aerial vehicle.

Connected through hyperedge HE-1 (induced by source paper "perception aware motion planning via multiobjective search on gpus"):
  with members:
    - "real time stochastic kinodynamic motion planning via multiobjective search on gpus"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "monte carlo motion planning for robot trajectory optimization under uncertainty"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"

Connected through hyperedge HE-2 (induced by source paper "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"):
  with members:
    - "data driven planning via imitation learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "pomdp lite for robust robot planning under uncertainty"
    - "configuration lattices for planar contact manipulation under uncertainty"
    - "learning to gather information via imitation"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "shared autonomy via hindsight optimization"
    - "on the complexity of solving markov decision problems"

Connected through hyperedge HE-3 (induced by source paper "deforestation prediction using neural networks and satellite imagery in a spatial information system"):
  with members:
    - "an artificial neural network for movement pattern analysis to estimate blood alcohol content level"
    - "building machines that learn and think like people"

Connected through hyperedge HE-4 (induced by source paper "low level active visual navigation increasing robustness of vision based localization using potential fields"):
  with members:
    - "direct sparse odometry"

Connected through hyperedge HE-5 (induced by source paper "feature based potential field for low level active visual navigation"):
  with members:
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "slam safe planner preventing monocular slam failure using reinforcement learning"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-7 (induced by source paper "learning to prevent monocular slam failure using reinforcement learning"):
  with members:
    - "orb slam a versatile and accurate monocular slam system"

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)