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: "pivot calibration concept for sensor attached mobile c arms"
  Abstract: Medical augmented reality has been actively studied for decades and many methods have been proposed torevolutionize clinical procedures. One example is the camera augmented mobile C-arm (CAMC), which providesa real-time video augmentation onto medical images by rigidly mounting and calibrating a camera to the imagingdevice. Since then, several CAMC variations have been suggested by calibrating 2D/3D cameras, trackers, andmore recently a Microsoft HoloLens to the C-arm. Different calibration methods have been applied to establishthe correspondence between the rigidly attached sensor and the imaging device. A crucial step for these methodsis the acquisition of X-Ray images or 3D reconstruction volumes; therefore, requiring the emission of ionizingradiation. In this work, we analyze the mechanical motion of the device and propose an alternatative methodto calibrate sensors to the C-arm without emitting any radiation. Given a sensor is rigidly attached to thedevice, we introduce an extended pivot calibration concept to compute the fixed translation from the sensor tothe C-arm rotation center. The fixed relationship between the sensor and rotation center can be formulated as apivot calibration problem with the pivot point moving on a locus. Our method exploits the rigid C-arm motiondescribing a Torus surface to solve this calibration problem. We explain the geometry of the C-arm motion andits relation to the attached sensor, propose a calibration algorithm and show its robustness against noise, as wellas trajectory and observed pose density by computer simulations. We discuss this geometric-based formulationand its potential extensions to different C-arm applications.

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)