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: "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.

Connected through hyperedge HE-1 (induced by source paper "on the sensory commutativity of action sequences for embodied agents"):
  with members:
    - "symmetry based disentangled representation learning requires interaction with environments"
    - "exploration by random network distillation"
    - "learning actionable representations with goal conditioned policies"
    - "unsupervised emergence of spatial structure from sensorimotor prediction"
    - "a non linear approach to space dimension perception by a naive agent"
    - "how many random seeds statistical power analysis in deep reinforcement learning experiments"
    - "learning an internal representation of the end effector configuration space"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised emergence of egocentric spatial structure from sensorimotor prediction"):
  with members:
    - "a non linear approach to space dimension perception by a naive agent"
    - "quantifying generalization in reinforcement learning"
    - "learning representations of spatial displacement through sensorimotor prediction"
    - "assessing generalization in deep reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning an internal representation of the end effector configuration space"
    - "towards a definition of disentangled representations"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"

Connected through hyperedge HE-3 (induced by source paper "learning representations of spatial displacement through sensorimotor prediction"):
  with members:
    - "state representation learning for control an overview"
    - "unsupervised state representation learning with robotic priors a robustness benchmark"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning to navigate in complex environments"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-4 (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"
    - "grounding object perception in a naive agent s sensorimotor experience"
    - "space as an invention of biological organisms"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "grounding object perception in a naive agent s sensorimotor experience"):
  with members:
    - "a non linear approach to space dimension perception by a naive agent"
    - "space as an invention of biological organisms"
    - "multi timescale nexting in a reinforcement learning robot"

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.LG(Machine Learning)