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: "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"
  Abstract: We introduce a new method that efficiently computes a set of viewpoints and trajectories for high-quality 3D reconstructions in outdoor environments. Our goal is to automatically explore an unknown area, and obtain a complete 3D scan of a region of interest (e.g., a large building). Images from a commodity RGB camera, mounted on an autonomously navigated quadcopter, are fed into a multi-view stereo reconstruction pipeline that produces high-quality results but is computationally expensive. In this setting, the scanning result is constrained by the restricted flight time of quadcopters. To this end, we introduce a novel optimization strategy that respects these constraints by maximizing the information gain from sparsely-sampled view points while limiting the total travel distance of the quadcopter. At the core of our method lies a hierarchical volumetric representation that allows the algorithm to distinguish between unknown, free, and occupied space. Furthermore, our information gain based formulation leverages this representation to handle occlusions in an efficient manner. In addition to the surface geometry, we utilize the free-space information to avoid obstacles and determine collision-free flight paths. Our tool can be used to specify the region of interest and to plan trajectories. We demonstrate our method by obtaining a number of compelling 3D reconstructions, and provide a thorough quantitative evaluation showing improvement over previous state-of-the-art and regular patterns.

Connected through hyperedge HE-1 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "the intelligent icu pilot study using artificial intelligence technology for autonomous patient monitoring"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "an unsupervised learning model for deformable medical image registration"
    - "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"

Connected through hyperedge HE-2 (induced by source paper "activemocap optimized drone flight for active human motion capture"):
  with members:
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "yolov3 an incremental improvement"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "optimizing for aesthetically pleasing quadrotor camera motion"
    - "human motion capture using a drone"
    - "deep directional statistics pose estimation with uncertainty quantification"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "harvesting multiple views for marker less 3d human pose annotations"

Connected through hyperedge HE-3 (induced by source paper "adaptive view planning for aerial 3d reconstruction"):
  with members:
    - "view selection with geometric uncertainty modeling"
    - "unrealcv connecting computer vision to unreal engine"
    - "orb slam a versatile and accurate monocular slam system"
    - "approximation algorithms for tsp with neighborhoods in the plane"

Connected through hyperedge HE-4 (induced by source paper "a smoothness energy without boundary distortion for curved surfaces"):
  with members:
    - "the vector heat method"
    - "functional characterization of deformation fields"
    - "natural boundary conditions for smoothing in geometry processing"
    - "geodesics in heat a new approach to computing distance based on heat flow"

Connected through hyperedge HE-5 (induced by source paper "learn to score efficient 3d scene exploration by predicting view utility"):
  with members:
    - "adaptive information gathering via imitation learning"
    - "submodular trajectory optimization for aerial 3d scanning"
    - "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-6 (induced by source paper "adaptive view planning for aerial 3d reconstruction of complex scenes"):
  with members:
    - "optimal reconstruction with a small number of views"
    - "submodular trajectory optimization for aerial 3d scanning"
    - "unrealcv connecting computer vision to unreal engine"
    - "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.CV(Computer Vision and Pattern Recognition)