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 an internal representation of the end effector configuration space"
  Abstract: Current machine learning techniques proposed to automatically discover a robot kinematics usually rely on a priori information about the robot's structure, sensors properties or end-effector position. This paper proposes a method to estimate a certain aspect of the forward kinematics model with no such information. An internal representation of the end-effector configuration is generated from unstructured proprioceptive and exteroceptive data flow under very limited assumptions. A mapping from the proprioceptive space to this representational space can then be used to control the robot.

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"
    - "flatland a lightweight first person 2 d environment for reinforcement learning"
    - "towards a definition of disentangled representations"

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"
    - "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-3 (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"
    - "learning representations of spatial displacement through sensorimotor prediction"
    - "unsupervised predictive memory in a goal directed agent"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "discovering latent states for model learning applying sensorimotor contingencies theory and predictive processing to model context"):
  with members:
    - "end to end training of deep visuomotor policies"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "where do goals come from a generic approach to autonomous goal system development"
    - "space as an invention of biological organisms"
    - "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"

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)