human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "glse precoders for massive mimo systems analysis and applications"):
  Abstract: This paper proposes the class of Generalized Least-Square-Error (GLSE) precoders for multiuser massive MIMO systems. For a generic transmit constellation, GLSE precoders minimize the interference at user terminals assuring that given constraints on the transmit signals are satisfied. The general form of these precoders enables us to impose multiple restrictions at the transmit signal such as limited peak power and restricted number of active transmit antennas. Invoking the replica method from statistical mechanics, we study the performance of GLSE precoders in the large-system limit. We show that the output symbols of these precoders are identically distributed and their statistics are described with an equivalent scalar GLSE precoder. Using the asymptotic results, we further address some applications of the GLSE precoders; namely forming transmit signals over a restricted alphabet and transmit antenna selection. Our investigations demonstrate that a computationally efficient GLSE precoder requires $41\%$ fewer active transmit antennas than conventional selection protocols in order to achieve a desired level of input-output distortion.

Includes members:
  Member "stepwise transmit antenna selection in downlink massive multiuser mimo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robustness of low complexity massive mimo architectures against passive eavesdropping")
  Member "nonlinear precoders for massive mimo systems with general constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure regularized zero forcing for multiuser mimome channels")
  Member "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "entropy and mutual information in models of deep neural networks")
  Member "asymptotics of nonlinear lse precoders with applications to transmit antenna selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robustness of low complexity massive mimo architectures against passive eavesdropping")
  Member "asymptotics of transmit antenna selection impact of multiple receive antennas":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robustness of low complexity massive mimo architectures against passive eavesdropping")
  Member "energy efficient coordinated multi cell multi group multicast beamforming with antenna selection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient transmission strategies for comp downlink overview extension and numerical comparison")
  Member "a new class of nonlinear precoders for hardware efficient massive mimo systems":
    also connected through other hyperedges:
      (none)
  Member "the replica symmetric prediction for compressed sensing with gaussian matrices is exact":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")

Question: Please tell me which class the source paper 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)