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: "pomdp lite for robust robot planning under uncertainty"
  Abstract: The partially observable Markov decision process (POMDP) provides a principled general model for planning under uncertainty. However, solving a general POMDP is computationally intractable in the worst case. This paper introduces POMDP-lite, a subclass of POMDPs in which the hidden state variables are constant or only change deterministically. We show that a POMDP-lite is equivalent to a set of fully observable Markov decision processes indexed by a hidden parameter and is useful for modeling a variety of interesting robotic tasks. We develop a simple model-based Bayesian reinforcement learning algorithm to solve POMDP-lite models. The algorithm performs well on large-scale POMDP-lite models with up to $10^{20}$ states and outperforms the state-of-the-art general-purpose POMDP algorithms. We further show that the algorithm is near-Bayesian-optimal under suitable conditions.

Connected through hyperedge HE-1 (induced by source paper "posterior sampling for anytime motion planning on graphs with expensive to evaluate edges"):
  with members:
    - "generalized lazy search for robot motion planning interleaving search and edge evaluation via event based toggles"
    - "leveraging experience in lazy search"
    - "lazy receding horizon a for efficient path planning in graphs with expensive to evaluate edges"
    - "motion planning networks"
    - "bayesian active edge evaluation on expensive graphs"
    - "near optimal edge evaluation in explicit generalized binomial graphs"
    - "a unifying formalism for shortest path problems with expensive edge evaluations via lazy best first search over paths with edge selectors"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts"):
  with members:
    - "meta learning of sequential strategies"
    - "lets drive driving in a crowd by learning from tree search"
    - "meta learners learning dynamics are unlike learners"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "residual policy learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-3 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments"):
  with members:
    - "search based optimal motion planning for automated driving"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "collaborative planning for mixed autonomy lane merging"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "carla an open urban driving simulator"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"

Connected through hyperedge HE-4 (induced by source paper "poddp partially observable differential dynamic programming for latent belief space planning"):
  with members:
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "deep variational reinforcement learning for pomdps"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "despot online pomdp planning with regularization"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"

Connected through hyperedge HE-5 (induced by source paper "robust and adaptive planning under model uncertainty"):
  with members:
    - "how should a robot assess risk towards an axiomatic theory of risk in robotics"
    - "simultaneous active parameter estimation and control using sampling based bayesian reinforcement learning"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "bayesian reinforcement learning a survey"
    - "exploration vs exploitation vs safety risk averse multi armed bandits"

Connected through hyperedge HE-6 (induced by source paper "an informative path planning framework for uav based terrain monitoring"):
  with members:
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"
    - "a data driven approach for autonomous motion planning and control in off road driving scenarios"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"
    - "dynamic system identification and control for a cost effective open source vtol mav"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"

Connected through hyperedge HE-7 (induced by source paper "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"):
  with members:
    - "data driven planning via imitation learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "configuration lattices for planar contact manipulation under uncertainty"
    - "perception aware path planning"
    - "learning to gather information via imitation"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "shared autonomy via hindsight optimization"
    - "on the complexity of solving markov decision problems"

Connected through hyperedge HE-8 (induced by source paper "estimation and control using sampling based bayesian reinforcement learning"):
  with members:
    - "simultaneous active parameter estimation and control using sampling based bayesian reinforcement learning"
    - "online algorithms for pomdps with continuous state action and observation spaces"
    - "on the complexity of solving markov decision problems"

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.AI(Artificial Intelligence)