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 "on the sensory commutativity of action sequences for embodied agents"):
  Abstract: We study perception in the scenario of an embodied agent equipped with first-person sensors and a continuous motor space with multiple degrees of freedom. Inspired by two theories of perception in artificial agents (Higgins (2018), Poincare (1895)) we consider theoretically the commutation properties of action sequences with respect to sensory information perceived by such embodied agent. From the theoretical derivations, we define the Sensory Commutativity Probability criterion which measures how much an agent's degree of freedom affects the environment in embodied scenarios. We empirically illustrate how it can be used to improve sample-efficiency in Reinforcement Learning.

Includes members:
  Member "symmetry based disentangled representation learning requires interaction with environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning group structure and disentangled representations of dynamical environments")
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "learning actionable representations with goal conditioned policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "unsupervised emergence of spatial structure from sensorimotor prediction":
    also connected through other hyperedges:
      (none)
  Member "a non linear approach to space dimension perception by a naive agent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised emergence of egocentric spatial structure from sensorimotor prediction")
  Member "how many random seeds statistical power analysis in deep reinforcement learning experiments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning")
  Member "learning an internal representation of the end effector configuration space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised emergence of egocentric spatial structure from sensorimotor prediction")
  Member "flatland a lightweight first person 2 d environment for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "s trigger continual state representation learning via self triggered generative replay")

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.AI(Artificial Intelligence)