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: "variational gaussian process state space models"
  Abstract: State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo. We also present stochastic variational inference and online learning approaches for fast learning with long time series.

Connected through hyperedge HE-1 (induced by source paper "fedloc federated learning framework for cooperative localization and location data processing"):
  with members:
    - "human motion trajectory prediction a survey"
    - "towards federated learning at scale system design"
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "on exact computation with an infinitely wide neural net"
    - "wireless traffic prediction with scalable gaussian process framework algorithms and verification"
    - "federated machine learning concept and applications"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "structured variational inference in unstable gaussian process state space models"):
  with members:
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "towards efficient full pose omnidirectionality with overactuated mavs"
    - "on calibration of modern neural networks"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "recurrent gaussian processes"
    - "translating videos to natural language using deep recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "learning nonlinear state space models with hamiltonian sequential monte carlo sampler"):
  with members:
    - "filtering variational objectives"
    - "importance weighted autoencoders"
    - "a recurrent latent variable model for sequential data"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "gaussian processes for big data"

Connected through hyperedge HE-4 (induced by source paper "combining physics based domain knowledge and machine learning using variational gaussian processes with explicit linear prior"):
  with members:
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "gaussian processes for big data"

Connected through hyperedge HE-5 (induced by source paper "neural dynamics discovery via gaussian process recurrent neural networks"):
  with members:
    - "a recurrent latent variable model for sequential data"
    - "recurrent gaussian processes"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-6 (induced by source paper "online gaussian process state space models learning and planning for partially observable dynamical systems"):
  with members:
    - "model based reinforcement learning with parametrized physical models and optimism driven exploration"

Connected through hyperedge HE-7 (induced by source paper "modeling neural dynamics during speech production using a state space variational autoencoder"):
  with members:
    - "lfads latent factor analysis via dynamical systems"

Connected through hyperedge HE-8 (induced by source paper "a data driven approach to dynamically adjust resource alocation for compute clusters"):
  with members:
    - "don t cry over spilled records memory elasticity of data parallel applications and its application to cluster scheduling"
    - "era a framework for economic resource allocation for the cloud"
    - "flexible scheduling of distributed analytic applications"
    - "revisiting size based scheduling with estimated job sizes"
    - "os assisted task preemption for hadoop"
    - "psbs practical size based scheduling"

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)