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 "q vae for disentangled representation learning and latent dynamical systems"):
  Abstract: This paper proposes a novel variational autoencoder (VAE) derived from Tsallis statistics, named q-VAE. A vanilla VAE is utilized to statistically extract latent space hidden in data sampled. Such latent space is useful to make robots controllable in feasible computational time and cost. To improve usefulness of the latent space, this paper focuses on disentangled representation learning like $\beta$-VAE, which is the baseline for it. Starting from the viewpoint of Tsallis statistics, a new lower bound of the q-VAE is derived to maximize likelihood of the data sampled. This can be regarded as an adaptive $\beta$-VAE with a deformed Kullback-Leibler divergence. To verify benefits from the q-VAE, a benchmark task to extract the latent space from MNIST dataset is performed. It is found that the q-VAE improved the disentangled representation while not deteriorating reconstruction accuracy of the data. As another advantage of the q-VAE, it does not require independency between the data. This advantage is demonstrated in learning latent dynamics of a nonlinear dynamical simulation. By combining the disentangled representation, the q-VAE achieves stable and accurate long-term state prediction from the initial state and the actions at respective times.

Includes members:
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "differentiable mpc for end to end planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "objective mismatch in model based reinforcement learning")
  Member "isolating sources of disentanglement in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "towards a definition of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised disentanglement without compromises")
  Member "searching for activation functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "sigmoid weighted linear units for neural network function approximation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "soft root sign activation function")
  Member "swish a self gated activation function":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning curiosity algorithms")
  Member "embed to control a locally linear latent dynamics model for control from raw images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)