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 "learning agent s spatial configuration from sensorimotor invariants"):
  Abstract: The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop truly autonomous robots, we must step away from this intuition and let robotic agents develop their own way of perceiving. The robot should start from scratch and gradually develop perceptual notions, under no prior assumptions, exclusively by looking into its sensorimotor experience and identifying repetitive patterns and invariants. One of the most fundamental perceptual notions, space, cannot be an exception to this requirement. In this paper we look into the prerequisites for the emergence of simplified spatial notions on the basis of a robot's sensorimotor flow. We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment. The environment-independent definition of space can be approached by looking into the functions that link the motor commands to changes in exteroceptive inputs. In a sufficiently rich environment, the kernels of these functions correspond uniquely to the spatial configuration of the agent's exteroceptors. We simulate a redundant robotic arm with a retina installed at its end-point and show how this agent can learn the configuration space of its retina. The resulting manifold has the topology of the Cartesian product of a plane and a circle, and corresponds to the planar position and orientation of the retina. Autonomous robots should develop perceptual notions from raw sensorimotor data.Environment-dependency of visual inputs complicates acquisition of spatial notions.Agent can learn its spatial configuration through invariants in sensorimotor laws.Approach is illustrated on a simulated planar multijoint agent with a mobile retina.

Includes members:
  Member "a non linear approach to space dimension perception by a naive agent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the sensory commutativity of action sequences for embodied agents")
  Member "learning an internal representation of the end effector configuration space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the sensory commutativity of action sequences for embodied agents")
  Member "where do goals come from a generic approach to autonomous goal system development":
    also connected through other hyperedges:
      (none)
  Member "space as an invention of biological organisms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning abstract perceptual notions the example of space")
  Member "fast damage recovery in robotics with the t resilience algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for single shot diagnosis and adaptation in damaged robots")
  Member "active learning of inverse models with intrinsically motivated goal exploration in robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling map elites to deep neuroevolution")
  Member "estimating uncertain spatial relationships in robotics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two body problem collaborative visual task completion")

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)