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: "autonomous grounding of visual field experience through sensorimotor prediction"
  Abstract: In a developmental framework, autonomous robots need to explore the world and learn how to interact with it. Without an a priori model of the system, this opens the challenging problem of having robots master their interface with the world: how to perceive their environment using their sensors, and how to act in it using their motors. The sensorimotor approach of perception claims that a naive agent can learn to master this interface by capturing regularities in the way its actions transform its sensory inputs. In this paper, we apply such an approach to the discovery and mastery of the visual field associated with a visual sensor. A computational model is formalized and applied to a simulated system 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"
    - "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"
    - "grounding object perception in a naive agent s sensorimotor experience"
    - "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"
    - "self learning and adaptation in a sensorimotor framework"
    - "grounding object perception in a naive agent s sensorimotor experience"
    - "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)