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: "gaussian process based decentralized data fusion and active sensing for mobility on demand system"
  Abstract: Mobility-on-demand (MoD) systems have recently emerged as a promising paradigm of one-way vehicle sharing for sustainable personal urban mobility in densely populated cities. In this paper, we enhance the capability of a MoD system by deploying robotic shared vehicles that can autonomously cruise the streets to be hailed by users. A key challenge to managing the MoD system effectively is that of real-time, fine-grained mobility demand sensing and prediction. This paper presents a novel decentralized data fusion and active sensing algorithm for real-time, fine-grained mobility demand sensing and prediction with a fleet of autonomous robotic vehicles in a MoD system. Our Gaussian process (GP)-based decentralized data fusion algorithm can achieve a fine balance between predictive power and time efficiency. We theoretically guarantee its predictive performance to be equivalent to that of a sophisticated centralized sparse approximation for the GP model: The computation of such a sparse approximate GP model can thus be distributed among the MoD vehicles, hence achieving efficient and scalable demand prediction. Though our decentralized active sensing strategy is devised to gather the most informative demand data for demand prediction, it can achieve a dual effect of fleet rebalancing to service the mobility demands. Empirical evaluation on real-world mobility demand data shows that our proposed algorithm can achieve a better balance between predictive accuracy and time efficiency than state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "implicit posterior variational inference for deep gaussian processes"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "collective online learning of gaussian processes in massive multi agent systems"
    - "adversarial distillation of bayesian neural network posteriors"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational inference for bayesian sparse gaussian process regression"
    - "wavenet a generative model for raw audio"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "persistent monitoring of stochastic spatio temporal phenomena with a small team of robots"):
  with members:
    - "persistent robotic tasks monitoring and sweeping in changing environments"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-3 (induced by source paper "collective online learning via decentralized gaussian processes in massive multi agent systems"):
  with members:
    - "gaussian processes for big data"

Connected through hyperedge HE-4 (induced by source paper "stochastic variational inference for bayesian sparse gaussian process regression"):
  with members:
    - "gp localize persistent mobile robot localization using online sparse gaussian process observation model"
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "gaussian processes for big data"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"
    - "active markov information theoretic path planning for robotic environmental sensing"

Connected through hyperedge HE-5 (induced by source paper "gp localize persistent mobile robot localization using online sparse gaussian process observation model"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"
    - "decision theoretic coordination and control for active multi camera surveillance in uncertain partially observable environments"
    - "active markov information theoretic path planning for robotic environmental sensing"

Connected through hyperedge HE-6 (induced by source paper "a queueing network approach to the analysis and control of mobility on demand systems"):
  with members:
    - "control of robotic mobility on demand systems a queueing theoretical perspective"

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.RO(Robotics)