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: "enabling a pepper robot to provide automated and interactive tours of a robotics laboratory"
  Abstract: The Pepper robot has become a widely recognised face for the perceived potential of social robots to enter our homes and businesses. However, to date, commercial and research applications of the Pepper have been largely restricted to roles in which the robot is able to remain stationary. This restriction is the result of a number of technical limitations, including limited sensing capabilities, and have as a result, reduced the number of roles in which use of the robot can be explored. In this paper, we present our approach to solving these problems, with the intention of opening up new research applications for the robot. To demonstrate the applicability of our approach, we have framed this work within the context of providing interactive tours of an open-plan robotics laboratory.

Connected through hyperedge HE-1 (induced by source paper "enabling socially competent navigation through incorporating hri"):
  with members:
    - "setting up pepper for autonomous navigation and personalized interaction with users"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "socially aware motion planning with deep reinforcement learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "towards dialogue based navigation with multivariate adaptation driven by intention and politeness for social robots"):
  with members:
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "sentiment adaptive end to end dialog systems"
    - "setting up pepper for autonomous navigation and personalized interaction with users"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "end to end joint learning of natural language understanding and dialogue manager"
    - "a computational approach to politeness with application to social factors"

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)