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: "space as an invention of biological organisms"
  Abstract: The question of the nature of space around us has occupied thinkers since the dawn of humanity, with scientists and philosophers today implicitly assuming that space is something that exists objectively. Here we show that this does not have to be the case: the notion of space could emerge when biological organisms seek an economic representation of their sensorimotor flow. The emergence of spatial notions does not necessitate the existence of real physical space, but only requires the presence of sensorimotor invariants called `compensable' sensory changes. We show mathematically and then in simulations that na\"ive agents making no assumptions about the existence of space are able to learn these invariants and to build the abstract notion that physicists call rigid displacement, which is independent of what is being displaced. Rigid displacements may underly perception of space as an unchanging medium within which objects are described by their relative positions. Our findings suggest that the question of the nature of space, currently exclusive to philosophy and physics, should also be addressed from the standpoint of neuroscience and artificial intelligence.

Connected through hyperedge HE-1 (induced by source paper "learning abstract perceptual notions the example of space"):
  with members:
    - "a non linear approach to space dimension perception by a naive agent"

Connected through hyperedge HE-2 (induced by source paper "a sensorimotor perspective on grounding the semantic of simple visual features"):
  with members:
    - "a non linear approach to space dimension perception by a naive agent"
    - "state representation learning for control an overview"
    - "identification of invariant sensorimotor structures as a prerequisite for the discovery of objects"
    - "grounding the experience of a visual field through sensorimotor contingencies"
    - "deep residual learning for image recognition"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-3 (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"
    - "learning agent s spatial configuration from sensorimotor invariants"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "learning agent s spatial configuration from sensorimotor invariants"):
  with members:
    - "a non linear approach to space dimension perception by a naive agent"
    - "learning an internal representation of the end effector configuration space"
    - "where do goals come from a generic approach to autonomous goal system development"
    - "fast damage recovery in robotics with the t resilience algorithm"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "estimating uncertain spatial relationships in robotics"

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"
    - "learning agent s spatial configuration from sensorimotor invariants"
    - "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.AI(Artificial Intelligence)