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: "frequency domain compressive channel estimation for frequency selective hybrid mmwave mimo systems"
  Abstract: Channel estimation is useful in millimeter wave (mmWave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics such as mutual information or signal-to-interference-noise (SINR) ratio. At mmWave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of prior work on channel estimation for hybrid architectures assumes a frequency-flat mmWave channel model. In this paper, we consider a frequency-selective mmWave channel and propose compressed-sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose trade-offs in complexity-overhead-performance as compared to those of previous approaches.

Connected through hyperedge HE-1 (induced by source paper "fully partially connected hybrid beamforming 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"
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "fully connected vs sub connected hybrid precoding architectures for mmwave mu mimo"
    - "millimeter wave mimo prototype measurements and experimental results"
    - "hybrid analog digital beamforming for massive mimo systems"
    - "a robust time domain beam alignment scheme for multi user wideband mmwave systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "efficient beam alignment for mmwave single carrier systems with hybrid mimo transceivers"):
  with members:
    - "single carrier modulation versus ofdm for millimeter wave wireless mimo"
    - "throughput optimal beam alignment in millimeter wave networks"
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "a robust time domain beam alignment scheme for multi user wideband mmwave systems"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "tracking mm wave channel dynamics fast beam training strategies under mobility"

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)