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 "phase noise compensation for ofdm systems"):
  Abstract: We describe a low-complexity method for time-domain compensation of phase noise in OFDM systems. We extend existing methods in several respects. First, we suggest using the Karhunen–Loeve representation of the phase noise process to estimate the phase noise. We then derive an improved data-directed choice of basis elements for LS phase noise estimation and present its total least square counterpart problem. The proposed method helps overcome one of the major weaknesses of OFDM systems. We also generalize the time-domain phase noise compensation to the multiuser MIMO context. Finally, we present simulation results using both simulated and measured phased noise. We quantify the tracking performance in the presence of residual carrier offset.

Includes members:
  Member "capacity of simo and miso phase noise channels with common separate oscillators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "massive mu mimo ofdm uplink with hardware impairments modeling and analysis")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "channel phase noise and frequency offset in ofdm systems joint estimation data detection and hybrid cramer rao lower bound":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "message passing based joint cfo and channel estimation in millimeter wave systems with one bit adcs")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "channel estimation carrier recovery and data detection in the presence of phase noise in ofdm relay systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint data detection and phase noise mitigation for light field video transmission in mimo ofdm systems")
  Member "understanding the impact of phase noise on active cancellation in wireless full duplex":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust transceiver design for full duplex decode and forward relay assisted mimo systems")

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.IT(Information Theory)