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: "state space lstm models with particle mcmc inference"
  Abstract: Long Short-Term Memory (LSTM) is one of the most powerful sequence models. Despite the strong performance, however, it lacks the nice interpretability as in state space models. In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL), which generalizes the earlier work \cite{zaheer2017latent} of combining topic models with LSTM. However, unlike \cite{zaheer2017latent}, we do not make any factorization assumptions in our inference algorithm. We present an efficient sampler based on sequential Monte Carlo (SMC) method that draws from the joint posterior directly. Experimental results confirms the superiority and stability of this SMC inference algorithm on a variety of domains.

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"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "video prediction for precipitation nowcasting"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "unsupervised discovery of parts structure and dynamics"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-3 (induced by source paper "sequential neural processes"):
  with members:
    - "attentive neural processes"
    - "learning latent dynamics for planning from pixels"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "learning and querying fast generative models for reinforcement learning"
    - "learning models for visual 3d localization with implicit mapping"
    - "generative temporal models with memory"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "variational temporal abstraction"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning latent dynamics for planning from pixels"
    - "learning and querying fast generative models for reinforcement learning"
    - "adaptive skip intervals temporal abstraction for recurrent dynamical models"
    - "time agnostic prediction predicting predictable video frames"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-5 (induced by source paper "inferring dynamical systems with long range dependencies through line attractor regularization"):
  with members:
    - "learning dynamical systems from partial observations"
    - "gated recurrent units viewed through the lens of continuous time dynamical systems"
    - "identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fmri"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "gated orthogonal recurrent units on learning to forget"
    - "recurrent orthogonal networks and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "particle based pedestrian path prediction using lstm mdl models"):
  with members:
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "deep learning driven visual path prediction from a single image"
    - "effective approaches to attention based neural machine translation"
    - "a recurrent latent variable model for sequential data"
    - "neural adaptive sequential monte carlo"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "forecasting individualized disease trajectories using interpretable deep learning"):
  with members:
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "attention based mixture density recurrent networks for history based recommendation"
    - "attention is all you need"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "the mythos of model interpretability"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "streaming video qoe modeling and prediction a long short term memory approach"):
  with members:
    - "modeling continuous video qoe evolution a state space approach"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural adaptive sequential monte carlo"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "modeling the time varying subjective quality of http video streams with rate adaptations"
    - "sequence to sequence learning with neural networks"
    - "nova qoe driven optimization of dash based video delivery in 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)