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 "amplitude and phase estimation for absolute calibration of massive mimo front ends"):
  Abstract: Massive multiple-input multiple-output (MIMO) promises significantly higher performance relative to conventional multiuser systems. However, the promised gains of massive MIMO systems rely heavily on the accuracy of the absolute front-end calibration, as well as quality of channel estimates at the base station (BS). In this paper, we analyze user equipment-aided calibration mechanism to estimate the amplitude scaling and phase drift at each radio-frequency chain interfacing with the BS array. Assuming a uniform linear array at the BS and Ricean fading, we obtain the estimation parameters with moment-based (amplitude, phase) and maximum-likelihood (phase-only) estimation techniques. In stark contrast to previous works, we mathematically articulate the equivalence of the two approaches for phase estimation. Furthermore, we rigorously derive a Cramer-Rao lower bound to characterize the accuracy of the two estimators. Via numerical simulations, we evaluate the estimator performance with varying dominant line-of-sight powers, dominant angles-of-arrival, and signal-to-noise ratios.

Includes members:
  Member "a framework for over the air reciprocity calibration for tdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for ul dl channel calibration in generic massive mimo systems")
  Member "impact of line of sight and unequal spatial correlation on uplink mu mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "massive mimo with spatially correlated rician fading channels")
  Member "reciprocity calibration for massive mimo proposal modeling and validation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for ul dl channel calibration in generic massive mimo systems")
  Member "hybrid beamforming for massive mimo a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "full duplex hybrid a d beamforming with reduced complexity multi tap analog cancellation")
  Member "massive mimo in real propagation environments do all antennas contribute equally":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "localization efficiency in massive mimo systems")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "scalable synchronization and reciprocity calibration for distributed multiuser mimo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "making cell free massive mimo competitive with mmse processing and centralized implementation")

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)