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: "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers"
  Abstract: This work focuses on the downlink and uplink of large-scale single-cell multiuser multiple-input multiple-output systems in which the base station (BS) endowed with M antennas communicates with K single-antenna user equipments (TIEs). Particularly, we aim at reducing the complexity of the linear precoder and receiver that maximize the minimum signal-to-interference-plus-noise ratio (SINR) subject to a given power constraint. To this end, we consider the asymptotic regime in which M and K grow large with a given ratio. Tools from random matrix theory (RMT) are then used to compute, in closed form, accurate approximations for the parameters of the optimal precoder and receiver, when imperfect channel state information (modeled by the generic Gauss- Markov formulation form) is available at the BS. The asymptotic analysis allows us to derive the asymptotically optimal linear precoder and receiver that are characterized by a lower complexity (due to the dependence on the large scale components of the channel) and, possibly, by a better resilience to imperfect channel state information. However, the implementation of both is still challenging as it requires fast inversions of large matrices in every coherence period. To overcome this issue, we apply the truncated polynomial expansion (TPE) technique to the precoding and receiving vector of each TIE and make use of RMT to determine the optimal weighting coefficients on a per-TIE basis that asymptotically solve the max-min SINR problem. Numerical results are used to validate the asymptotic analysis in the finite system regime and to show that the proposed TPE transceivers efficiently mimic the optimal ones, while requiring much lower computational complexity.

Connected through hyperedge HE-1 (induced by source paper "asymptotic max min sinr analysis of reconfigurable intelligent surface assisted miso systems"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "asymptotic analysis of rzf over double scattering channels with mmse estimation"
    - "rfocus practical beamforming for small devices"
    - "resource allocation for secure irs assisted multiuser miso systems"
    - "enabling secure wireless communications via intelligent reflecting surfaces"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "mimo assisted networks relying on large intelligent surfaces a stochastic geometry model"

Connected through hyperedge HE-2 (induced by source paper "sum throughput maximization in multi tag backscattering to multiantenna reader"):
  with members:
    - "optimal channel estimation for reciprocity based backscattering with a full duplex mimo reader"
    - "cooperative ambient backscatter communications for green internet of things"
    - "fractional programming for communication systems part i power control and beamforming"
    - "retrodirective large antenna energy beamforming in backscatter multi user networks"
    - "transmit precoding and receive power splitting for harvested power maximization in mimo swipt systems"
    - "inference from randomized transmissions by many backscatter sensors"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "multi antenna wireless energy transfer for backscatter communication systems"

Connected through hyperedge HE-3 (induced by source paper "enhanced max min sinr for uplink cell free massive mimo systems"):
  with members:
    - "cell free massive mimo with limited backhaul"
    - "cooperative access networks optimum fronthaul quantization in distributed massive mimo and cloud ran"
    - "energy efficient power control a look at 5g wireless technologies"
    - "cell free massive mimo versus small cells"
    - "multiobjective signal processing optimization the way to balance conflicting metrics in 5g systems"

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)