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: "grounding object perception in a naive agent s sensorimotor experience"
  Abstract: Artificial object perception usually relies on a priori defined models and feature extraction algorithms. We study how the concept of object can be grounded in the sensorimotor experience of a naive agent. Without any knowledge about itself or the world it is immersed in, the agent explores its sensorimotor space and identifies objects as consistent networks of sensorimotor transitions, independent from their context. A fundamental drive for prediction is assumed to explain the emergence of such networks from a developmental standpoint. An algorithm is proposed and tested to illustrate the approach.

Connected through hyperedge HE-1 (induced by source paper "identification of invariant sensorimotor structures as a prerequisite for the discovery of objects"):
  with members:
    - "interactive perception leveraging action in perception and perception in action"
    - "context discovery for model learning in partially observable environments"
    - "autonomous grounding of visual field experience through sensorimotor prediction"
    - "the curious robot learning visual representations via physical interactions"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "learning image representations equivariant to ego motion"
    - "you only look once unified real time object detection"
    - "recent advances in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "grounding the experience of a visual field through sensorimotor contingencies"):
  with members:
    - "grounding perception a developmental approach to sensorimotor contingencies"
    - "autonomous grounding of visual field experience through sensorimotor prediction"
    - "self learning and adaptation in a sensorimotor framework"
    - "learning agent s spatial configuration from sensorimotor invariants"
    - "space as an invention of biological organisms"
    - "representation learning a review and new perspectives"

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)