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: "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
  Abstract: A key problem of robotic environmental sensing and monitoring is that of active sensing: How can a team of robots plan the most informative observation paths to minimize the uncertainty in modeling and predicting an environmental phenomenon? This paper presents two principled approaches to efficient information-theoretic path planning based on entropy and mutual information criteria for in situ active sensing of an important broad class of widely-occurring environmental phenomena called anisotropic fields. Our proposed algorithms are novel in addressing a trade-off between active sensing performance and time efficiency. An important practical consequence is that our algorithms can exploit the spatial correlation structure of Gaussian process-based anisotropic fields to improve time efficiency while preserving near-optimal active sensing performance. We analyze the time complexity of our algorithms and prove analytically that they scale better than state-of-the-art algorithms with increasing planning horizon length. We provide theoretical guarantees on the active sensing performance of our algorithms for a class of exploration tasks called transect sampling, which, in particular, can be improved with longer planning time and/or lower spatial correlation along the transect. Empirical evaluation on real-world anisotropic field data shows that our algorithms can perform better or at least as well as the state-of-the-art algorithms while often incurring a few orders of magnitude less computational time, even when the field conditions are less favorable.

Connected through hyperedge HE-1 (induced by source paper "informative path planning for mobile sensing with reinforcement learning"):
  with members:
    - "informative path planning for location fingerprint collection"
    - "learning combinatorial optimization algorithms over graphs"
    - "turf fast data collection for fingerprint based indoor localization"
    - "neural combinatorial optimization with reinforcement learning"
    - "informative planning and online learning with sparse gaussian processes"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "multi robot routing for persistent monitoring with latency constraints"):
  with members:
    - "submodular trajectory optimization for aerial 3d scanning"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "persistent monitoring in discrete environments minimizing the maximum weighted latency between observations"

Connected through hyperedge HE-4 (induced by source paper "model based active source identification in complex environments"):
  with members:
    - "distributed state estimation using intermittently connected robot networks"
    - "ergodic exploration of distributed information"

Connected through hyperedge HE-5 (induced by source paper "learning a spatial field in minimum time with a team of robots"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "bayesian optimal active search and surveying"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-6 (induced by source paper "real time area coverage and target localization using receding horizon ergodic exploration"):
  with members:
    - "communication free distributed coverage for networked systems"
    - "autonomous visual rendering using physical motion"
    - "dynamic task execution using active parameter identification with the baxter research robot"
    - "iterative sequential action control for stable model based control of nonlinear systems"
    - "sequential action control closed form optimal control for nonlinear and nonsmooth systems"
    - "ergodic exploration of distributed information"
    - "persistent robotic tasks monitoring and sweeping in changing environments"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-7 (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"
    - "gaussian process based decentralized data fusion and active sensing for mobility on demand system"
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "gaussian processes for big data"
    - "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-8 (induced by source paper "data driven learning and planning for environmental sampling"):
  with members:
    - "informative planning and online learning with sparse gaussian processes"
    - "active markov information theoretic path planning for robotic environmental sensing"

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)