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 "a learning approach for low complexity optimization of energy efficiency in multicarrier wireless networks"):
  Abstract: This paper proposes computationally efficient algorithms to maximize the energy efficiency in multicarrier wireless interference networks, by a suitable allocation of the system radio resources, namely, the transmit powers and subcarrier assignment. The problem is formulated as the maximization of the system global energy efficiency subject to both maximum power and minimum rate constraints. This leads to a challenging nonconvex fractional problem, which is tackled through an interplay of fractional programming, learning, and game theory. The proposed algorithmic framework is provably convergent and has a complexity linear in both the number of users and subcarriers, whereas other available solutions can only guarantee a polynomial complexity in the number of users and subcarriers. Numerical results show that the proposed method performs similarly as other, more complex, algorithms.

Includes members:
  Member "a brief introduction to machine learning for engineers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from learning to meta learning reduced training overhead and complexity for communication systems")
  Member "energy efficient power control a look at 5g wireless technologies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "a survey of energy efficient techniques for 5g networks and challenges ahead":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed stochastic optimization via matrix exponential learning")
  Member "transmit without regrets online optimization in mimo ofdm cognitive radio systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed stochastic optimization via matrix exponential learning")
  Member "distributed learning policies for power allocation in multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low complexity distributed radio access network slicing algorithms and experimental results")
  Member "framework for link level energy efficiency optimization with informed transmitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed power control with partial channel state information performance characterization and design")
  Member "energy efficient power control in impulse radio uwb wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "energy efficient power control a look at 5g wireless technologies")

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)