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 "dynamics learning with cascaded variational inference for multi step manipulation"):
  Abstract: The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal. We present Cascaded Variational Inference (CAVIN) Planner, a model-based method that hierarchically generates plans by sampling from latent spaces. To facilitate planning over long time horizons, our method learns latent representations that decouple the prediction of high-level effects from the generation of low-level motions through cascaded variational inference. This enables us to model dynamics at two different levels of temporal resolutions for hierarchical planning. We evaluate our approach in three multi-step robotic manipulation tasks in cluttered tabletop environments given high-dimensional observations. Empirical results demonstrate that the proposed method outperforms state-of-the-art model-based methods by strategically interacting with multiple objects.

Includes members:
  Member "mechanical search multi step retrieval of a target object occluded by clutter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward sim to real directional semantic grasping")
  Member "learning action representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "leaky tiling activations a simple approach to learning sparse representations online")
  Member "robot motion planning in learned latent spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "learning task oriented grasping for tool manipulation from simulated self supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward sim to real directional semantic grasping")
  Member "learning synergies between pushing and grasping with self supervised deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")
  Member "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "multi modal imitation learning from unstructured demonstrations using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "machine learning approaches for motor learning a short review")
  Member "combining self supervised learning and imitation for vision based rope manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation")

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.RO(Robotics)