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 "centralized power control in cognitive radio networks using modulation and coding classification feedback"):
  Abstract: In this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The learning process enabler is a cooperative Modulation and Coding Classification (MCC) technique which estimates the Modulation and Coding scheme (MCS) of the PU. Due to the lack of cooperation between the PU and the CRN, the CRN exploits this multilevel MCC sensing feedback as implicit channel state information (CSI) of the PU link in order to constantly monitor the impact of the aggregated interference it causes. In this paper, an algorithm is developed for maximizing the CRN throughput (the PC optimization objective) and simultaneously learning how to mitigate PU interference (the optimization problem constraint) by using only the MCC information. Ideal approaches for this problem setting with high convergence rate are the cutting plane methods (CPM). Here, we focus on the analytic center cutting plane method (ACCPM) and the center of gravity cutting plane method (CGCPM) whose effectiveness in the proposed simultaneous PC and interference channel learning algorithm is demonstrated through numerical simulations.

Includes members:
  Member "energy beamforming with one bit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "the one bit null space learning algorithm and its convergence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed wireless power transfer with energy feedback")
  Member "distributed transmit beamforming using feedback control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low complexity random rotation based schemes for intelligent reflecting surfaces")

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)