human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adaptive predictive power management for mobile lte devices"):
  Abstract: Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G standards. In addition to improving the power efficiency of components through structural and technological advances, optimizing the energy efficiency through improved dynamic power management is an integral part in contemporary hardware design. Most techniques targeting mobile devices proposed so far, however, are purely reactive in powering down and up system components. Promising approaches extend this, by predicting and using information from the environment and the communication protocol to take proactive decisions. In this paper, we propose and compare two proactive algorithmic approaches for light-weight machine learning to predict the control information needed to allow a mobile device to go to sleep states more often, e.g., in time slots of transmission inactivity in a cell. The first approach is based on supervised learning, the second one based on reinforcement learning. As the implementation of learning techniques also creates energy and resource costs, both approaches are carefully evaluated not only in terms of prediction accuracy, but also overall energy savings. Using the presented technique, we observe achievable energy savings of up to 17%.

Includes members:
  Member "a simple reinforcement learning mechanism for resource allocation in lte a networks with markov decision process and q learning":
    also connected through other hyperedges:
      (none)
  Member "on the difficulty of training recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch")

Question: Please tell me which class the source paper 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.NI(Networking and Internet Architecture)