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: "motion planning under partial observability using game based abstraction"
  Abstract: We study motion planning problems where agents move inside environments that are not fully observable and subject to uncertainties. The goal is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. Such problems are naturally modelled by partially observable Markov decision processes (POMDPs). Because of the potentially huge or even infinite belief space of POMDPs, verification and strategy synthesis is in general computationally intractable. We tackle this difficulty by exploiting typical structural properties of such scenarios; for instance, we assume that agents have the ability to observe their own positions inside an environment. Ambiguity in the state of the environment is abstracted into non-deterministic choices over the possible states of the environment. Technically, this abstraction transforms POMDPs into probabilistic two-player games (PGs). For these PGs, efficient verification tools are able to determine strategies that approximate certain measures on the POMDP. If an approximation is too coarse to provide guarantees, an abstraction refinement scheme further resolves the belief space of the POMDP. We demonstrate that our method improves the state of the art by orders of magnitude compared to a direct solution of the POMDP.

Connected through hyperedge HE-1 (induced by source paper "strategy synthesis in pomdps via game based abstractions"):
  with members:
    - "human in the loop synthesis for partially observable markov decision processes"
    - "counterexample guided abstraction refinement for pomdps"
    - "a storm is coming a modern probabilistic model checker"
    - "reinforcement learning of pomdps using spectral methods"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "discretized approximations for pomdp with average cost"
    - "control of probabilistic systems under dynamic partially known environments with temporal logic specifications"

Connected through hyperedge HE-2 (induced by source paper "human in the loop synthesis for partially observable markov decision processes"):
  with members:
    - "permissive finite state controllers of pomdps using parameter synthesis"
    - "a storm is coming a modern probabilistic model checker"
    - "synthesis of shared control protocols with provable safety and performance guarantees"
    - "high level counterexamples for probabilistic automata"
    - "optimal cost almost sure reachability in pomdps"
    - "qualitative analysis of pomdps with temporal logic specifications for robotics applications"
    - "solving pomdps by searching the space of finite policies"
    - "heuristic search value iteration 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.RO(Robotics)