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: "efficient beam alignment for mmwave single carrier systems with hybrid mimo transceivers"
  Abstract: Communication at millimeter wave (mmWave) bands is expected to become a key ingredient of next generation (5G) wireless networks. Effective mmWave communications require fast and reliable methods for beamforming at both the User Equipment (UE) and the Base Station (BS) sides, in order to achieve a sufficiently large Signal-to-Noise Ratio (SNR) after beamforming. We refer to the problem of finding a pair of strongly coupled narrow beams at the transmitter and receiver as the Beam Alignment (BA) problem. In this paper, we propose an efficient BA scheme for single-carrier mmWave communications. In the proposed scheme, the BS periodically probes the channel in the downlink via a pre-specified pseudo-random beamforming codebook and pseudo-random spreading codes, letting each UE estimate the Angle-of-Arrival / Angle-of-Departure (AoA-AoD) pair of the multipath channel for which the energy transfer is maximum. We leverage the sparse nature of mmWave channels in the AoA-AoD domain to formulate the BA problem as the estimation of a sparse non-negative vector. Based on the recently developed Non-Negative Least Squares (NNLS) technique, we efficiently find the strongest AoA-AoD pair connecting each UE to the BS. We evaluate the performance of the proposed scheme under a realistic channel model, where the propagation channel consists of a few multipath scattering components each having different delays, AoAs-AoDs, and Doppler shifts.The channel model parameters are consistent with experimental channel measurements. Simulation results indicate that the proposed method is highly robust to fast channel variations caused by the large Doppler spread between the multipath components. Furthermore, we also show that after achieving BA the beamformed channel is essentially frequency-flat, such that single-carrier communication needs no equalization in the time domain.

Connected through hyperedge HE-1 (induced by source paper "queue aware beam scheduling for half duplex mmwave relay networks"):
  with members:
    - "fully partially connected hybrid beamforming architectures for mmwave mu mimo"
    - "gaussian 1 2 1 networks capacity results for mmwave communications"
    - "a new backpressure algorithm for joint rate control and routing with vanishing utility optimality gaps and finite queue lengths"
    - "towards fast convergence low delay and low complexity network optimization"
    - "efficiently finding simple schedules in gaussian half duplex relay line networks"

Connected through hyperedge HE-2 (induced by source paper "fast beam alignment for millimeter wave communications a sparse encoding and phaseless decoding approach"):
  with members:
    - "millimeter wave channel estimation via exploiting joint sparse and low rank structures"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "adaptive millimeter wave beam alignment for dual polarized mimo systems"

Connected through hyperedge HE-3 (induced by source paper "fully connected vs sub connected hybrid precoding architectures for mmwave mu mimo"):
  with members:
    - "hybrid precoding architecture for massive multiuser mimo with dissipation sub connected or fully connected structures"
    - "on the energy efficiency of mimo hybrid beamforming for millimeter wave systems with nonlinear power amplifiers"
    - "channel reconstruction based hybrid precoding for millimeter wave multi user mimo systems"
    - "hybrid beamforming for massive mimo a survey"

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)