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: "deep learning in robotics a review of recent research"
  Abstract: Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least thirty papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-a-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics.

Connected through hyperedge HE-1 (induced by source paper "deep learning on mobile devices a review"):
  with members:
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the limits and potentials of deep learning for robotics"
    - "ai benchmark running deep neural networks on android smartphones"
    - "transfer learning for time series classification"
    - "an analysis of deep neural network models for practical applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "toward robustness against label noise in training deep discriminative neural networks"

Connected through hyperedge HE-2 (induced by source paper "motion generation considering situation with conditional generative adversarial networks for throwing robots"):
  with members:
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "robust adversarial reinforcement learning"
    - "improved training of wasserstein gans"
    - "one shot imitation learning"
    - "generative adversarial imitation learning"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "epopt learning robust neural network policies using model ensembles"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-3 (induced by source paper "transferring adaptive theory of mind to social robots insights from developmental psychology to robotics"):
  with members:
    - "functional advantages of an adaptive theory of mind for robotics a review of current architectures"
    - "machine theory of mind"
    - "building machines that learn and think like people"
    - "online real time multiple spatiotemporal action localisation and prediction"

Connected through hyperedge HE-4 (induced by source paper "comparing computing platforms for deep learning on a humanoid robot"):
  with members:
    - "child sized 3d printed igus humanoid open platform"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "microsoft coco common objects in context"

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)