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: "neural adaptive sequential monte carlo"
  Abstract: Sequential Monte Carlo (SMC), or particle filtering, is a popular class of methods for sampling from an intractable target distribution using a sequence of simpler intermediate distributions. Like other importance sampling-based methods, performance is critically dependent on the proposal distribution: a bad proposal can lead to arbitrarily inaccurate estimates of the target distribution. This paper presents a new method for automatically adapting the proposal using an approximation of the Kullback-Leibler divergence between the true posterior and the proposal distribution. The method is very flexible, applicable to any parameterized proposal distribution and it supports online and batch variants. We use the new framework to adapt powerful proposal distributions with rich parameterizations based upon neural networks leading to Neural Adaptive Sequential Monte Carlo (NASMC). Experiments indicate that NASMC significantly improves inference in a non-linear state space model outperforming adaptive proposal methods including the Extended Kalman and Unscented Particle Filters. Experiments also indicate that improved inference translates into improved parameter learning when NASMC is used as a subroutine of Particle Marginal Metropolis Hastings. Finally we show that NASMC is able to train a latent variable recurrent neural network (LV-RNN) achieving results that compete with the state-of-the-art for polymorphic music modelling. NASMC can be seen as bridging the gap between adaptive SMC methods and the recent work in scalable, black-box variational inference.

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 "multi person pose tracking using sequential monte carlo with probabilistic neural pose predictor"):
  with members:
    - "pose estimator and tracker using temporal flow maps for limbs"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "uncertainty aware imitation learning using kernelized movement primitives"
    - "uncertainty aware learning from demonstrations in multiple contexts using bayesian neural networks"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "multi domain pose network for multi person pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "a fusion approach for multi frame optical flow estimation"

Connected through hyperedge HE-3 (induced by source paper "multiverse causal reasoning using importance sampling in probabilistic programming"):
  with members:
    - "counterfactual off policy evaluation with gumbel max structural causal models"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "pyro deep universal probabilistic programming"
    - "local rule based explanations of black box decision systems"
    - "counterfactual multi agent policy gradients"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "using synthetic data to train neural networks is model based reasoning"

Connected through hyperedge HE-4 (induced by source paper "meta learning mcmc proposals"):
  with members:
    - "learning to optimize neural nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "signal based bayesian seismic monitoring"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "inference compilation and universal probabilistic programming"
    - "neurally guided procedural models amortized inference for procedural graphics programs using neural networks"
    - "importance weighted autoencoders"

Connected through hyperedge HE-5 (induced by source paper "universal marginaliser for deep amortised inference for probabilistic programs"):
  with members:
    - "learning about an exponential amount of conditional distributions"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "pyro deep universal probabilistic programming"
    - "attention is all you need"
    - "using synthetic data to train neural networks is model based reasoning"
    - "inference compilation and universal probabilistic programming"
    - "deep amortized inference for probabilistic programs"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-6 (induced by source paper "approximate inference in discrete distributions with monte carlo tree search and value functions"):
  with members:
    - "meta learning mcmc proposals"
    - "etalumis bringing probabilistic programming to scientific simulators at scale"
    - "relational inductive biases deep learning and graph networks"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "equivalence between policy gradients and soft q learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"

Connected through hyperedge HE-7 (induced by source paper "coupled variational recurrent collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sequential variational autoencoders for collaborative filtering"
    - "a hybrid variational autoencoder for collaborative filtering"
    - "recurrent poisson factorization for temporal recommendation"
    - "neural collaborative filtering"
    - "discrete event continuous time rnns"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "streaming recommender systems"

Connected through hyperedge HE-8 (induced by source paper "particle filter networks with application to visual localization"):
  with members:
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "filtering variational objectives"
    - "value prediction network"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "optnet differentiable optimization as a layer in neural networks"

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)