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: "multi agent deep reinforcement learning for zero energy communities"
  Abstract: Advances in renewable energy generation and introduction of the government targets to improve energy efficiency gave rise to a concept of a Zero Energy Building (ZEB). A ZEB is a building whose net energy usage over a year is zero, i.e., its energy use is not larger than its overall renewables generation. A collection of ZEBs forms a Zero Energy Community (ZEC). This paper addresses the problem of energy sharing in such a community. This is different from previously addressed energy sharing between buildings as our focus is on the improvement of community energy status, while traditionally research focused on reducing losses due to transmission and storage, or achieving economic gains. We model this problem in a multi-agent environment and propose a Deep Reinforcement Learning (DRL) based solution. Each building is represented by an intelligent agent that learns over time the appropriate behaviour to share energy. We have evaluated the proposed solution in a multi-agent simulation built using osBrain. Results indicate that with time agents learn to collaborate and learn a policy comparable to the optimal policy, which in turn improves the ZEC's energy status. Buildings with no renewables preferred to request energy from their neighbours rather than from the supply grid.

Connected through hyperedge HE-1 (induced by source paper "a review of reinforcement learning for autonomous building energy management"):
  with members:
    - "deep reinforcement learning based optimal control of hot water systems"
    - "using reinforcement learning for demand response of domestic hot water buffers a real life demonstration"
    - "on line building energy optimization using deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"
    - "learning agent for a heat pump thermostat with a set back strategy using model free reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent deep reinforcement learning with human strategies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "a human mixed strategy approach to deep reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using 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)