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: "a scalable method for solving high dimensional continuous pomdps using local approximation"
  Abstract: Partially-Observable Markov Decision Processes (POMDPs) are typically solved by finding an approximate global solution to a corresponding belief-MDP. In this paper, we offer a new planning algorithm for POMDPs with continuous state, action and observation spaces. Since such domains have an inherent notion of locality, we can find an approximate solution using local optimization methods. We parameterize the belief distribution as a Gaussian mixture, and use the Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is a first-order filter, we can marginalize over the observations analytically. By using feedback control and state estimation during policy execution, we recover a behavior that is effectively conditioned on incoming observations despite the unconditioned planning. Local optimization provides no guarantees of global optimality, but it allows us to tackle domains that are at least an order of magnitude larger than the current state-of-the-art. We demonstrate the scalability of our algorithm by considering a simulated hand-eye coordination domain with 16 continuous state dimensions and 6 continuous action dimensions.

Connected through hyperedge HE-1 (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"
    - "pomdp lite for robust robot planning under uncertainty"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-2 (induced by source paper "perception aware autonomous mast motion planning for planetary exploration rovers"):
  with members:
    - "bi directional value learning for risk aware planning under uncertainty"
    - "low level active visual navigation increasing robustness of vision based localization using potential fields"
    - "despot online pomdp planning with regularization"
    - "efficient planning under uncertainty with macro actions"
    - "sampling based algorithms for optimal motion planning"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-3 (induced by source paper "optimal continuous state pomdp planning with semantic observations a variational approach"):
  with members:
    - "closed loop bayesian semantic data fusion for collaborative human autonomy target search"
    - "near optimal belief space planning via t lqg"
    - "deep value of information estimators for collaborative human machine information gathering"
    - "bandit algorithms for tree search"
    - "approximate safety verification and control of partially observable stochastic hybrid systems"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-4 (induced by source paper "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-5 (induced by source paper "efficient planning under uncertainty with macro actions"):
  with members:
    - "approximate planning for factored pomdps using belief state simplification"
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "policy contingent abstraction for robust robot control"
    - "online planning algorithms for pomdps"

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)