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: "optimal demand response using device based reinforcement learning"
  Abstract: Demand response (DR) for residential and small commercial buildings is estimated to account for as much as 65% of the total energy savings potential of DR, and previous work shows that a fully automated Energy Management System (EMS) is a necessary prerequisite to DR in these areas. In this paper, we propose a novel EMS formulation for DR problems in these sectors. Specifically, we formulate a fully automated EMS's rescheduling problem as a reinforcement learning (RL) problem, and argue that this RL problem can be approximately solved by decomposing it over device clusters. Compared with existing formulations, our new formulation (1) does not require explicitly modeling the user's dissatisfaction on job rescheduling, (2) enables the EMS to self-initiate jobs, (3) allows the user to initiate more flexible requests and (4) has a computational complexity linear in the number of devices. We also demonstrate the simulation results of applying Q-learning, one of the most popular and classical RL algorithms, to a representative example.

Connected through hyperedge HE-1 (induced by source paper "online residential demand response via contextual multi armed bandits"):
  with members:
    - "introduction to multi armed bandits"
    - "a tutorial on thompson sampling"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-3 (induced by source paper "action assembly sparse imitation learning for text based games with combinatorial action spaces"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "towards solving text based games by producing adaptive action spaces"
    - "counting to explore and generalize in text based games"
    - "an actor critic algorithm for sequence prediction"
    - "deep reinforcement learning in large discrete action spaces"
    - "deep reinforcement learning with a natural language action space"
    - "language understanding for text based games using deep reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "multi agent deep reinforcement learning for zero energy communities"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-6 (induced by source paper "direct load control of thermostatically controlled loads based on sparse observations using deep reinforcement learning"):
  with members:
    - "building model identification during regular operation empirical results and challenges"
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"
    - "plato policy learning using adaptive trajectory optimization"

Connected through hyperedge HE-7 (induced by source paper "model free control of thermostatically controlled loads connected to a district heating network"):
  with members:
    - "the use of distributed thermal storage in district heating grids for demand side management"
    - "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"

Connected through hyperedge HE-8 (induced by source paper "convolutional neural networks for automatic state time feature extraction in reinforcement learning applied to residential load control"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "experimental analysis of data driven control for a building heating system"
    - "rmsprop and equilibrated adaptive learning rates for non convex optimization"
    - "end to end training of deep visuomotor policies"

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)