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: "permissive finite state controllers of pomdps using parameter synthesis"
  Abstract: We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs). #R##N#The key insight is that computing (randomized) FSCs on POMDPs is equivalent to synthesis for parametric Markov chains (pMCs). #R##N#This correspondence enables using parameter synthesis techniques to compute FSCs for POMDPs in a black-box fashion. #R##N#We investigate how typical restrictions on parameter values affect the quality of the obtained FSCs. #R##N#Permissive strategies for POMDPs are obtained as regions of parameter values, a natural output of parameter synthesis techniques. #R##N#Experimental evaluation on several POMDP benchmarks shows promising results.

Connected through hyperedge HE-1 (induced by source paper "human in the loop synthesis for partially observable markov decision processes"):
  with members:
    - "a storm is coming a modern probabilistic model checker"
    - "motion planning under partial observability using game based abstraction"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "privacy verification in pomdps via barrier certificates"):
  with members:
    - "verification of uncertain pomdps using barrier certificates"
    - "pomdp model learning for human robot collaboration"
    - "privacy preserving controller synthesis via belief abstraction"
    - "value function approximations for partially observable markov decision processes"

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.LO(Logic in Computer Science)