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: "fast reinforcement learning for energy efficient wireless communication"
  Abstract: We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g., multimedia data) over a fading channel. We propose a rigorous and unified framework for simultaneously utilizing both physical-layer and system-level techniques to minimize energy consumption, under delay constraints, in the presence of stochastic and unknown traffic and channel conditions. We formulate the problem as a Markov decision process and solve it online using reinforcement learning. The advantages of the proposed online method are that i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal physical-layer and system-level power management strategies; ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "towards tactile internet in beyond 5g era recent advances current issues and future directions"):
  with members:
    - "emerging edge computing technologies for distributed iot systems"
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "scalable intelligence enabled networking with traffic engineering in 5g scenarios for future audio visual tactile internet"
    - "distributed optimization for energy efficient fog computing in the tactile internet"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "robust multi path communications for uavs in the urban iot"

Connected through hyperedge HE-2 (induced by source paper "accelerated structure aware reinforcement learning for delay sensitive energy harvesting wireless sensors"):
  with members:
    - "structural properties of optimal transmission policies for delay sensitive energy harvesting wireless sensors"
    - "aerial uav iot sensing for ubiquitous immersive communication and virtual human teleportation"
    - "viewport adaptive navigable 360 degree video delivery"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal packet scheduling in an energy harvesting communication system"

Connected through hyperedge HE-3 (induced by source paper "context aware hierarchical online learning for performance maximization in mobile crowdsourcing"):
  with members:
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "dynamic assessments matching and allocation of tasks"
    - "distributed online learning in social recommender systems"
    - "online clustering of bandits"
    - "resourceful contextual bandits"
    - "distributed online learning via cooperative contextual bandits"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (induced by source paper "structural properties of optimal transmission policies for delay sensitive energy harvesting wireless sensors"):
  with members:
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal packet scheduling in an energy harvesting communication system"
    - "optimal energy management policies for energy harvesting sensor nodes"

Connected through hyperedge HE-5 (induced by source paper "distributed and centralized hybrid csma ca tdma schemes for single hop wireless networks"):
  with members:
    - "reinforcement learning a survey"

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)