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 "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"):
  Abstract: Energy-aware system design is an important optimization task for static and mobile Internet of Things (IoT)-based sensor nodes, especially for highly resource-constrained vehicles such as mobile robotic systems. For 4G/5G-based cellular communication systems, the effective transmission power of uplink data transmissions is of crucial importance for the overall system power consumption. Unfortunately, this information is usually hidden within off-the-shelf modems and mobile handsets and can therefore not be exploited for enabling green communication. Moreover, the dynamic transmission power control behavior of the mobile device is not even explicitly modeled in most of the established simulation frameworks. In this paper, we present a novel machine learning-based approach for forecasting the resulting uplink transmission power used for data transmissions based on the available passive network quality indicators and application-level information. The model is derived from comprehensive field measurements of drive tests performed in a public cellular network and can be parameterized for integrating all measurements a given target platform is able to provide into the prediction process. In a comparison of three different machine learning methods, Random-Forest models thoroughly performed best with a mean average error of 3.166 dB. As the absolute sum of errors converges towards zero and falls below 1 dB after 28 predictions in average, the approach is well-suited for long-term power estimations.

Includes members:
  Member "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer")
  Member "discover your competition in lte client based passive data rate prediction by machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards cooperative data rate prediction for future mobile and vehicular 6g networks")
  Member "srslte an open source platform for lte evolution and experimentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pbe cc congestion control via endpoint centric physical layer bandwidth measurements")
  Member "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")

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)