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 representations of spatial displacement through sensorimotor prediction"
  Abstract: Robots act in their environment through sequences of continuous motor commands. Because of the dimensionality of the motor space, as well as the infinite possible combinations of successive motor commands, agents need compact representations that capture the structure of the resulting displacements. In the case of an autonomous agent with no a priori knowledge about its sensorimotor apparatus, this compression has to be learned. We propose to use Recurrent Neural Networks to encode motor sequences into a compact representation, which is used to predict the consequence of motor sequences in term of sensory changes. We show that sensory prediction can successfully guide the compression of motor sequences into representations that are organized topologically in term of spatial displacement.

Connected through hyperedge HE-1 (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"
    - "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"
    - "self normalizing neural networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised emergence of spatial structure from sensorimotor prediction"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "world models"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning an internal representation of the end effector configuration space"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "self normalizing neural networks"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"

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)