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 "stochastic variational inference for bayesian sparse gaussian process regression"):
  Abstract: This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with various corresponding correlation structures of the observation noises. Our variational Bayesian SGPR (VBSGPR) models jointly treat both the distributions of the inducing variables and hyperparameters as variational parameters, which enables the decomposability of the variational lower bound that in turn can be exploited for stochastic optimization. Such a stochastic optimization involves iteratively following the stochastic gradient of the variational lower bound to improve its estimates of the optimal variational distributions of the inducing variables and hyperparameters (and hence the predictive distribution) of our VBSGPR models and is guaranteed to achieve asymptotic convergence to them. We show that the stochastic gradient is an unbiased estimator of the exact gradient and can be computed in constant time per iteration, hence achieving scalability to big data. We empirically evaluate the performance of our proposed framework on two real-world, massive datasets.

Includes members:
  Member "gp localize persistent mobile robot localization using online sparse gaussian process observation model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "implicit posterior variational inference for deep gaussian processes")
  Member "gaussian process based decentralized data fusion and active sensing for mobility on demand system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "implicit posterior variational inference for deep gaussian processes")
  Member "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "implicit posterior variational inference for deep gaussian processes")
  Member "gaussian processes for big data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")
  Member "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "informative path planning for mobile sensing with reinforcement learning")
  Member "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "implicit posterior variational inference for deep gaussian processes")
  Member "active markov information theoretic path planning for robotic environmental sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implicit posterior variational inference for deep gaussian processes")

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)