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 "energy inspired models learning with sampler induced distributions"):
  Abstract: Energy-based models (EBMs) are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function. As a result, inference in EBMs relies on approximate sampling algorithms, leading to a mismatch between the model and inference. Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model. This yields a class of energy-inspired models (EIMs) that incorporate learned energy functions while still providing exact samples and tractable log-likelihood lower bounds. We describe and evaluate three instantiations of such models based on truncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling. These models outperform or perform comparably to the recently proposed Learned Accept/Reject Sampling algorithm and provide new insights on ranking Noise Contrastive Estimation and Contrastive Predictive Coding. Moreover, EIMs allow us to generalize a recent connection between multi-sample variational lower bounds and auxiliary variable variational inference. We show how recent variational bounds can be unified with EIMs as the variational family.

Includes members:
  Member "implicit generation and generalization in energy based models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "divide and couple using monte carlo variational objectives for posterior approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic differentiation variational inference with mixtures")
  Member "importance weighting and variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning discrete distributions by dequantization")
  Member "kernel exponential family estimation via doubly dual embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "training deep energy based models with f divergence minimization")
  Member "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "hamiltonian variational auto encoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pi vae encoding stochastic process priors with variational autoencoders")
  Member "learning descriptor networks for 3d shape synthesis and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning single image 3d reconstruction by generative modelling of shape pose and shading")
  Member "stochastic video generation with a learned prior":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

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)