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: "flexible pilot contamination mitigation with doppler psd alignment"
  Abstract: Pilot contamination in the uplink (UL) can severely degrade the channel estimation quality at the base station in a massive multi-input multi-output (MIMO) system. Thus, it is critical to explore all possible avenues to enable more orthogonal resources for the users to transmit noninterfering UL pilots. In conventional designs, pilot orthogonality typically assumes constant channel gains over time, which limits the amount of orthogonal resources in the case of time-selective channels. To circumvent this constraint, in this paper, we show how to enable orthogonal multiplexing of pilots in the case of Doppler fading by aligning the power spectrum densities (PSDs) of different users. From the derived PSD aligning rules, we can see multiple users can be sounded simultaneously without creating/suffering pilot contamination even when these users are experiencing time-varying channels. Furthermore, we provide analytical formulas characterizing the channel estimation mean square error performance. Computer simulations further confirm us the PSD alignment can serve as one important decontamination mechanism for the UL pilots in massive MIMO.

Connected through hyperedge HE-1 (induced by source paper "a scalable framework for csi feedback in fdd massive mimo via dl path aligning"):
  with members:
    - "pilot decontamination via pdp alignment"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "achievable rates of fdd massive mimo systems with spatial channel correlation"
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

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)