human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "grounding the experience of a visual field through sensorimotor contingencies"):
  Abstract: Abstract   Artificial perception is traditionally handled by hand-designing task specific algorithms. However, a truly autonomous robot should develop perceptive abilities on its own, by interacting with its environment, and adapting to new situations. The sensorimotor contingencies theory proposes to ground the development of those perceptive abilities in the way the agent can actively transform its sensory inputs. We propose a sensorimotor approach, inspired by this theory, in which the agent explores the world and discovers its properties by capturing the sensorimotor regularities they induce. This work presents an application of this approach to the discovery of a so-called visual field as the set of regularities that a visual sensor imposes on a naive agent’s experience. A formalism is proposed to describe how those regularities can be captured in a sensorimotor predictive model. Finally, the approach is evaluated on a simulated system coarsely inspired from the human retina.

Includes members:
  Member "grounding perception a developmental approach to sensorimotor contingencies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "context discovery for model learning in partially observable environments")
  Member "autonomous grounding of visual field experience through sensorimotor prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identification of invariant sensorimotor structures as a prerequisite for the discovery of objects")
  Member "self learning and adaptation in a sensorimotor framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep predictive policy training using reinforcement learning")
  Member "grounding object perception in a naive agent s sensorimotor experience":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identification of invariant sensorimotor structures as a prerequisite for the discovery of objects")
  Member "learning agent s spatial configuration from sensorimotor invariants":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the sensory commutativity of action sequences for embodied agents")
  Member "space as an invention of biological organisms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning abstract perceptual notions the example of space")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nestedvae isolating common factors via weak supervision")

Question: Please tell me which class the source paper 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.LG(Machine Learning)