human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "filtering variational objectives"
  Abstract: When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.

Connected through hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "graph normalizing flows"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "emerging convolutions for generative normalizing flows"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"

Connected through hyperedge HE-2 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue"):
  with members:
    - "long and diverse text generation with planning based hierarchical variational model"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-3 (induced by source paper "lazy object copy as a platform for population based probabilistic programming"):
  with members:
    - "pyro deep universal probabilistic programming"
    - "design and implementation of probabilistic programming language anglican"
    - "a compilation target for probabilistic programming languages"
    - "venture a higher order probabilistic programming platform with programmable inference"

Connected through hyperedge HE-4 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "importance weighting and variational inference"
    - "kernel exponential family estimation via doubly dual embedding"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "hamiltonian variational auto encoder"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-5 (induced by source paper "customizing sequence generation with multi task dynamical systems"):
  with members:
    - "multi task time series analysis applied to drug response modelling"
    - "quaternet a quaternion based recurrent model for human motion"
    - "disentangled sequential autoencoder"
    - "learning to decompose and disentangle representations for video prediction"
    - "stable recurrent models"
    - "decomposing motion and content for natural video sequence prediction"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-6 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "representation balancing mdps for off policy policy evaluation"
    - "distributed prioritized experience replay"
    - "deep variational reinforcement learning for pomdps"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "distributed distributional deterministic policy gradients"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"

Connected through hyperedge HE-7 (induced by source paper "stcn stochastic temporal convolutional networks"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "stochastic wavenet a generative latent variable model for sequential data"
    - "fast decoding in sequence models using discrete latent variables"
    - "deepwriting making digital ink editable via deep generative modeling"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning hierarchical features from generative models"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "the variational infomax autoencoder"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "infovae information maximizing variational autoencoders"
    - "fixing a broken elbo"
    - "deep variational information bottleneck"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"
    - "variational lossy autoencoder"

Question: Please tell me which class the center node 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)