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 "bidirectional helmholtz machines"):
  Abstract: Efficient unsupervised training and inference in deep generative models remains a challenging problem. One basic approach, called Helmholtz machine, involves training a top-down directed generative model together with a bottom-up auxiliary model used for approximate inference. Recent results indicate that better generative models can be obtained with better approximate inference procedures. Instead of improving the inference procedure, we here propose a new model which guarantees that the top-down and bottom-up distributions can efficiently invert each other. We achieve this by interpreting both the top-down and the bottom-up directed models as approximate inference distributions and by defining the model distribution to be the geometric mean of these two. We present a lower-bound for the likelihood of this model and we show that optimizing this bound regularizes the model so that the Bhattacharyya distance between the bottom-up and top-down approximate distributions is minimized. This approach results in state of the art generative models which prefer significantly deeper architectures while it allows for orders of magnitude more efficient approximate inference.

Includes members:
  Member "importance weighted autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "blocks and fuel frameworks for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics")
  Member "deep unsupervised learning using nonequilibrium thermodynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training deep energy based models with f divergence minimization")
  Member "iterative refinement of approximate posterior for training directed belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inference suboptimality in variational autoencoders")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "reweighted wake sleep":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta amortized variational inference and learning")
  Member "neural variational inference and learning in belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "estimating gradients for discrete random variables by sampling without replacement")
  Member "accurate and conservative estimates of mrf log likelihood using reverse annealing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "implicit generation and generalization in energy based models")

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)