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: "a framework for over the air reciprocity calibration for tdd massive mimo systems"
  Abstract: One of the biggest challenges in operating massive multiple-input multiple-output systems is the acquisition of accurate channel state information at the transmitter. To take up this challenge, time division duplex is more favorable thanks to its channel reciprocity between downlink and uplink. However, while the propagation channel over the air is reciprocal, the radio-frequency front-ends in the transceivers are not. Therefore, calibration is required to compensate the RF hardware asymmetry. #R##N#Although various over-the-air calibration methods exist to address the above problem, this paper offers a unified representation of these algorithms, providing a higher level view on the calibration problem, and introduces innovations on calibration methods. We present a novel family of calibration methods, based on antenna grouping, which improve accuracy and speed up the calibration process compared to existing methods. We then provide the Cram\'er-Rao bound as the performance evaluation benchmark and compare maximum likelihood and least squares estimators. We also differentiate between coherent and non-coherent accumulation of calibration measurements, and point out that enabling non-coherent accumulation allows the training to be spread in time, minimizing impact to the data service. Overall, these results have special value in allowing to design reciprocity calibration techniques that are both accurate and resource-effective.

Connected through hyperedge HE-1 (induced by source paper "amplitude and phase estimation for absolute calibration of massive mimo front ends"):
  with members:
    - "impact of line of sight and unequal spatial correlation on uplink mu mimo systems"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "hybrid beamforming for massive mimo a survey"
    - "massive mimo in real propagation environments do all antennas contribute equally"
    - "massive mimo for next generation wireless systems"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"

Connected through hyperedge HE-2 (induced by source paper "deep learning for ul dl channel calibration in generic massive mimo systems"):
  with members:
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions"
    - "neural offset min sum decoding"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "learning to communicate channel auto encoders domain specific regularizers and attention"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "achieving phase coherency and gain stability in active antenna arrays for sub 6 ghz fdd and tdd fd mimo challenges and solutions"):
  with members:
    - "channel reciprocity calibration in tdd hybrid beamforming massive mimo systems"
    - "toward a realistic assessment of multiple antenna hcns residual additive transceiver hardware impairments and channel aging"
    - "overview of full dimension mimo in lte advanced pro"
    - "massive mimo performance with imperfect channel reciprocity and channel estimation error"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"

Connected through hyperedge HE-4 (induced by source paper "beyond 5g leveraging cell free tdd massive mimo using cascaded deep learning"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "massive mimo performance with imperfect channel reciprocity and channel estimation error"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "cell free massive mimo versus small cells"
    - "reciprocity calibration for massive mimo proposal modeling and validation"

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)