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: "value of structural health monitoring quantification in partially observable stochastic environments"
  Abstract: Sequential decision-making under uncertainty for optimal life-cycle control of deteriorating engineering systems and infrastructure entails two fundamental classes of decisions. The first class pertains to the various structural interventions, which can directly modify the existing properties of the system, while the second class refers to prescribing appropriate inspection and monitoring schemes, which are essential for updating our existing knowledge about the system states. The latter have to rely on quantifiable measures of efficiency, determined on the basis of objective criteria that, among others, consider the Value of Information (VoI) of different observational strategies, and the Value of Structural Health Monitoring (VoSHM) over the entire system life-cycle. In this work, we present general solutions for quantifying the VoI and VoSHM in partially observable stochastic domains, and although our definitions and methodology are general, we are particularly emphasizing and describing the role of Partially Observable Markov Decision Processes (POMDPs) in solving this problem, due to their advantageous theoretical and practical attributes in estimating arbitrarily well globally optimal policies. POMDP formulations are articulated for different structural environments having shared intervention actions but diversified inspection and monitoring options, thus enabling VoI and VoSHM estimation through their differentiated stochastic optimal control policies. POMDP solutions are derived using point-based solvers, which can efficiently approximate the POMDP value functions through Bellman backups at selected reachable points of the belief space. The suggested methodology is applied on stationary and non-stationary deteriorating environments, with both infinite and finite planning horizons, featuring single- or multi-component engineering systems.

Connected through hyperedge HE-1 (induced by source paper "managing engineering systems with large state and action spaces through deep reinforcement learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "action branching architectures for deep reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "sample efficient actor critic with experience replay"
    - "asynchronous methods for deep reinforcement learning"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "benchmarking deep reinforcement learning for continuous control"
    - "prioritized experience replay"

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)