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 "plannable approximations to mdp homomorphisms equivariance under actions"):
  Abstract: This work exploits action equivariance for representation learning in reinforcement learning. Equivariance under actions states that transitions in the input space are mirrored by equivalent transitions in latent space, while the map and transition functions should also commute. We introduce a contrastive loss function that enforces action equivariance on the learned representations. We prove that when our loss is zero, we have a homomorphism of a deterministic Markov Decision Process (MDP). Learning equivariant maps leads to structured latent spaces, allowing us to build a model on which we plan through value iteration. We show experimentally that for deterministic MDPs, the optimal policy in the abstract MDP can be successfully lifted to the original MDP. Moreover, the approach easily adapts to changes in the goal states. Empirically, we show that in such MDPs, we obtain better representations in fewer epochs compared to representation learning approaches using reconstructions, while generalizing better to new goals than model-free approaches.

Includes members:
  Member "unsupervised grounding of plannable first order logic representation from images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "making sense of sensory input")
  Member "learning robotic manipulation through visual planning and acting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")
  Member "mastering atari go chess and shogi by planning with a learned model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neuroevolution of self interpretable agents")
  Member "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entity abstraction in visual model based reinforcement learning")
  Member "unsupervised state representation learning in atari":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predictive coding for locally linear control")
  Member "deepmdp learning continuous latent space models for representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neuroevolution of self interpretable agents")
  Member "efficient model based deep reinforcement learning with variational state tabulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "frequency based search control in dyna")
  Member "composable planning with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bayesian relational memory for semantic visual navigation")

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.LG(Machine Learning)