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: "systems of natural language facilitated human robot cooperation a review"
  Abstract: Natural-language-facilitated human-robot cooperation (NLC), in which natural language (NL) is used to share knowledge between a human and a robot for conducting intuitive human-robot cooperation (HRC), is continuously developing in the recent decade. Currently, NLC is used in several robotic domains such as manufacturing, daily assistance and health caregiving. It is necessary to summarize current NLC-based robotic systems and discuss the future developing trends, providing helpful information for future NLC research. In this review, we first analyzed the driving forces behind the NLC research. Regarding to a robot s cognition level during the cooperation, the NLC implementations then were categorized into four types {NL-based control, NL-based robot training, NL-based task execution, NL-based social companion} for comparison and discussion. Last based on our perspective and comprehensive paper review, the future research trends were discussed.

Connected through hyperedge HE-1 (induced by source paper "dwnet deep wide network for 3d action recognition"):
  with members:
    - "random vector functional link neural network based ensemble deep learning"
    - "graph neural networks a review of methods and applications"
    - "graph edge convolutional neural networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"

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)