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: "referencing between a head mounted device and robotic manipulators"
  Abstract: Having a precise and robust transformation between the robot coordinate system and the AR-device coordinate system is paramount during human-robot interaction (HRI) based on augmented reality using Head mounted displays (HMD), both for intuitive information display and for the tracking of human motions. Most current solutions in this area rely either on the tracking of visual markers, e.g. QR codes, or on manual referencing, both of which provide unsatisfying results. Meanwhile a plethora of object detection and referencing methods exist in the wider robotic and machine vision communities. The precision of the referencing is likewise almost never measured. Here we would like to address this issue by firstly presenting an overview of currently used referencing methods between robots and HMDs. This is followed by a brief overview of object detection and referencing methods used in the field of robotics. Based on these methods we suggest three classes of referencing algorithms we intend to pursue - semi-automatic, on-shot; automatic, one-shot; and automatic continuous. We describe the general workflows of these three classes as well as describing our proposed algorithms in each of these classes. Finally we present the first experimental results of a semi-automatic referencing algorithm, tested on an industrial KUKA KR-5 manipulator.

Connected through hyperedge HE-1 (induced by source paper "concepts for end to end augmented reality based human robot interaction systems"):
  with members:
    - "general hand guidance framework using microsoft hololens"
    - "alternative modes of interaction in proximal human in the loop operation of robots"
    - "ganerated hands for real time 3d hand tracking from monocular rgb"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fully convolutional networks for semantic segmentation"

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)