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: "channel reconstruction based hybrid precoding for millimeter wave multi user mimo systems"
  Abstract: The focus of this paper is on multi-user multi-input multi-output transmissions for millimeter-wave systems with a hybrid precoding architecture at the base station. To enable multiuser transmissions, the base station uses a cell-specific codebook of beamforming vectors over an initial  beam alignment  phase. Each user uses a user-specific codebook of beamforming vectors to learn the top-  $P$    (where   $P \geq 1$  ) beam pairs in terms of the observed signal-to-noise ratio (  ${\text{SNR}}$   ) in a single-user setting. The top-  $P$    beam indices along with their   ${\text{SNR}}$   s are fed back from each user and the base station leverages this information to generate beam weights for simultaneous transmissions. A typical method to generate the beam weights is to use  only   the best beam for each user and either steer energy along this beam, or to utilize this information to reduce multi-user interference. The other beams are used as fall-back options to address blockage or mobility. Such an approach completely discards information learned about the channel condition(s) even though each user feeds back this information. With this background, this paper develops an advanced  directional  precoding structure for simultaneous transmissions at the cost of an additional marginal feedback overhead. This construction relies on three main innovations: first, additional feedback to allow the base station to  reconstruct  a rank-   $P$   approximation of the channel matrix between it and each user; second, a zero-forcing structure that leverages this information to combat multi-user interference by remaining agnostic of the receiver beam knowledge in the precoder design; and third, a hybrid precoding architecture that allows  both  amplitude and phase control at low complexity and cost to allow the implementation of the zero-forcing structure. Numerical studies show that the proposed scheme results in a significant sum rate performance improvement over naive schemes even with a coarse initial beam alignment codebook.

Connected through hyperedge HE-1 (induced by source paper "evolution of physical layer communications research in the post 5g era"):
  with members:
    - "statistical blockage modeling and robustness of beamforming in millimeter wave systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "millimeter wave mimo prototype measurements and experimental results"
    - "directional beamforming for millimeter wave mimo systems"
    - "beamforming tradeoffs for initial ue discovery in millimeter wave mimo systems"

Connected through hyperedge HE-2 (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"
    - "efficient beam alignment for mmwave single carrier systems with hybrid mimo transceivers"
    - "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)