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: "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
  Abstract: Recent research in robot exploration and mapping has focused on sampling environmental hotspot fields. This exploration task is formalized by Low, Dolan, and Khosla (2008) in a sequential decision-theoretic planning under uncertainty framework called MASP. The time complexity of solving MASP approximately depends on the map resolution, which limits its use in large-scale, high-resolution exploration and mapping. To alleviate this computational difficulty, this paper presents an information-theoretic approach to MASP (iMASP) for efficient adaptive path planning; by reformulating the cost-minimizing iMASP as a reward-maximizing problem, its time complexity becomes independent of map resolution and is less sensitive to increasing robot team size as demonstrated both theoretically and empirically. Using the reward-maximizing dual, we derive a novel adaptive variant of maximum entropy sampling, thus improving the induced exploration policy performance. It also allows us to establish theoretical bounds quantifying the performance advantage of optimal adaptive over non-adaptive policies and the performance quality of approximately optimal vs. optimal adaptive policies. We show analytically and empirically the superior performance of iMASP-based policies for sampling the log-Gaussian process to that of policies for the widely-used Gaussian process in mapping the hotspot field. Lastly, we provide sufficient conditions that, when met, guarantee adaptivity has no benefit under an assumed environment model.

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 "learning a spatial field in minimum time with a team of robots"):
  with members:
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "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-3 (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"
    - "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-4 (induced by source paper "gp localize persistent mobile robot localization using online sparse gaussian process observation model"):
  with members:
    - "gaussian process based decentralized data fusion and active sensing for mobility on demand system"
    - "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-5 (induced by source paper "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"):
  with members:
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "near optimal brl using optimistic local transitions"
    - "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
    - "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 "gaussian process based decentralized data fusion and active sensing for mobility on demand system"):
  with members:
    - "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-7 (induced by source paper "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"):
  with members:
    - "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 "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"):
  with members:
    - "decentralized sensor fusion with distributed particle filters"
    - "robust probabilistic inference in distributed systems"
    - "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)