human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "massive mimo performance tdd versus fdd what do measurements say"
  Abstract: Downlink beamforming in Massive MIMO either relies on uplink pilot measurements - exploiting reciprocity and TDD operation, or on the use of a predetermined grid of beams with user equipments reporting their preferred beams, mostly in FDD operation. Massive MIMO in its originally conceived form uses the first strategy, with uplink pilots, whereas there is currently significant commercial interest in the second, grid-of-beams. It has been analytically shown that in isotropic scattering (independent Rayleigh fading) the first approach outperforms the second. Nevertheless there remains controversy regarding their relative performance in practice. In this contribution, the performances of these two strategies are compared using measured channel data at 2.6 GHz.

Connected through hyperedge HE-1 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "multi resolution csi feedback with deep learning in massive mimo system"
    - "deep convolutional compression for massive mimo csi feedback"
    - "machine learning in the air"

Connected through hyperedge HE-2 (induced by source paper "code domain noma in massive mimo when is it needed"):
  with members:
    - "what is the benefit of code domain noma in massive mimo"
    - "what role can noma play in massive mimo"
    - "gaussian message passing for overloaded massive mimo noma"
    - "massive mimo has unlimited capacity"
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "fundamental limits of low density spreading noma with fading"
    - "massive mimo performance evaluation based on measured propagation data"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"

Connected through hyperedge HE-3 (induced by source paper "massive mimo for internet of things iot connectivity"):
  with members:
    - "massive mimo unsourced random access"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "non stationarities in extra large scale massive mimo"
    - "ubiquitous cell free massive mimo communications"
    - "sparcs for unsourced random access"
    - "management of network slicing in 5g radio access networks functional framework and information models"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"

Connected through hyperedge HE-4 (induced by source paper "multi cell hybrid millimeter wave systems pilot contamination and interference mitigation"):
  with members:
    - "a multi beam noma framework for hybrid mmwave systems"
    - "massive mimo has unlimited capacity"
    - "optimal resource allocation for power efficient mc noma with imperfect channel state information"
    - "coverage analysis for millimeter wave networks the impact of directional antenna arrays"
    - "where when and how mmwave is used in 5g and beyon"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "small scale local area and transitional millimeter wave propagation for 5g communications"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"

Connected through hyperedge HE-5 (induced by source paper "channel reciprocity calibration in tdd hybrid beamforming massive mimo systems"):
  with members:
    - "overview of full dimension mimo in lte advanced pro"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"

Question: Please tell me which class the center node 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)