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 deep q network for the beer game a deep reinforcement learning algorithm to solve inventory optimization problems"
  Abstract: The beer game is a widely used in-class game that is played in supply chain management classes to demonstrate the bullwhip effect. The game is a decentralized, multi-agent, cooperative problem that can be modeled as a serial supply chain network in which agents cooperatively attempt to minimize the total cost of the network even though each agent can only observe its own local information. Each agent chooses order quantities to replenish its stock. Under some conditions, a base-stock replenishment policy is known to be optimal. However, in a decentralized supply chain in which some agents (stages) may act irrationally (as they do in the beer game), there is no known optimal policy for an agent wishing to act optimally. #R##N#We propose a machine learning algorithm, based on deep Q-networks, to optimize the replenishment decisions at a given stage. When playing alongside agents who follow a base-stock policy, our algorithm obtains near-optimal order quantities. It performs much better than a base-stock policy when the other agents use a more realistic model of human ordering behavior. Unlike most other algorithms in the literature, our algorithm does not have any limits on the beer game parameter values. Like any deep learning algorithm, training the algorithm can be computationally intensive, but this can be performed ahead of time; the algorithm executes in real time when the game is played. Moreover, we propose a transfer learning approach so that the training performed for one agent and one set of cost coefficients can be adapted quickly for other agents and costs. Our algorithm can be extended to other decentralized multi-agent cooperative games with partially observed information, which is a common type of situation in real-world supply chain problems.

Connected through hyperedge HE-1 (induced by source paper "don t forget your teacher a corrective reinforcement learning framework"):
  with members:
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "a lyapunov based approach to safe reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "kickstarting deep reinforcement learning"
    - "ai safety gridworlds"
    - "divide and conquer reinforcement learning"
    - "constrained policy optimization"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "orl reinforcement learning benchmarks for online stochastic optimization problems"):
  with members:
    - "learning dexterous in hand manipulation"
    - "distributed prioritized experience replay"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "rllib abstractions for distributed reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "proximal policy optimization algorithms"
    - "neural combinatorial optimization with reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-3 (induced by source paper "can sophisticated dispatching strategy acquired by reinforcement learning a case study in dynamic courier dispatching system"):
  with members:
    - "efficient large scale fleet management via multi agent deep reinforcement learning"

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)