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 "neural embedding for physical manipulations"):
  Abstract: In common real-world robotic operations, action and state spaces can be vast and sometimes unknown, and observations are often relatively sparse. How do we learn the full topology of action and state spaces when given only few and sparse observations? Inspired by the properties of grid cells in mammalian brains, we build a generative model that enforces a normalized pairwise distance constraint between the latent space and output space to achieve data-efficient discovery of output spaces. This method achieves substantially better results than prior generative models, such as Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs). Prior models have the common issue of mode collapse and thus fail to explore the full topology of output space. We demonstrate the effectiveness of our model on various datasets both qualitatively and quantitatively.

Includes members:
  Member "mcp learning composable hierarchical control with multiplicative compositional policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "normalized diversification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multimodal image outpainting with regularized normalized diversification")
  Member "interpretable latent spaces for learning from demonstration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "preventing imitation learning with adversarial policy ensembles")
  Member "hierarchical visuomotor control of humanoids":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reusable neural skill embeddings for vision guided whole body movement and object manipulation")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "latent space policies for hierarchical reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning for combinatorial optimization a survey")
  Member "diversity is all you need learning skills without a reward function":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning curiosity algorithms")
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to walk in the real world with minimal human effort")

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)