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: "submodular trajectory optimization for aerial 3d scanning"
  Abstract: Drones equipped with cameras are emerging as a powerful tool for large-scale aerial 3D scanning, but existing automatic flight planners do not exploit all available information about the scene, and can therefore produce inaccurate and incomplete 3D models. We present an automatic method to generate drone trajectories, such that the imagery acquired during the flight will later produce a high-fidelity 3D model. Our method uses a coarse estimate of the scene geometry to plan camera trajectories that: (1) cover the scene as thoroughly as possible; (2) encourage observations of scene geometry from a diverse set of viewing angles; (3) avoid obstacles; and (4) respect a user-specified flight time budget. Our method relies on a mathematical model of scene coverage that exhibits an intuitive diminishing returns property known as submodularity. We leverage this property extensively to design a trajectory planning algorithm that reasons globally about the non-additive coverage reward obtained across a trajectory, jointly with the cost of traveling between views. We evaluate our method by using it to scan three large outdoor scenes, and we perform a quantitative evaluation using a photorealistic video game simulator.

Connected through hyperedge HE-1 (induced by source paper "multi robot routing for persistent monitoring with latency constraints"):
  with members:
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "persistent monitoring in discrete environments minimizing the maximum weighted latency between observations"

Connected through hyperedge HE-2 (induced by source paper "coverage path planning using path primitive sampling and primitive coverage graph for visual inspection"):
  with members:
    - "heterogeneous vehicles routing for water canal damage assessment"
    - "a reinforcement learning approach to the view planning problem"

Connected through hyperedge HE-3 (induced by source paper "near optimal path planning for complex robotic inspection tasks"):
  with members:
    - "interactive camera network design using a virtual reality interface"

Connected through hyperedge HE-4 (induced by source paper "learn to score efficient 3d scene exploration by predicting view utility"):
  with members:
    - "adaptive information gathering via imitation learning"
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "generic 3d representation via pose estimation and matching"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a reinforcement learning approach to the view planning problem"
    - "identity mappings in deep residual networks"
    - "octnet learning deep 3d representations at high resolutions"

Connected through hyperedge HE-5 (induced by source paper "adaptive view planning for aerial 3d reconstruction of complex scenes"):
  with members:
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
    - "optimal reconstruction with a small number of views"
    - "unrealcv connecting computer vision to unreal engine"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-6 (induced by source paper "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"):
  with members:
    - "active image based modeling with a toy drone"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "sampling based algorithms for optimal motion planning"

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)