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 "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"):
  Abstract: This paper considers a low-complexity Gaussian message passing iterative detection (GMPID) algorithm for a massive multiuser multiple-input multiple-output (MU-MIMO) system, in which a base station with    $M$    antennas serves    $K$    Gaussian sources simultaneously. Both    $K$    and    $M$    are very large numbers, and we consider the cases that    $K  . The GMPID is a message passing algorithm operating on a fully connected loopy graph, which is well understood to be non-convergent in some cases. As it is hard to analyze the GMPID directly, the large-scale property of the massive MU-MIMO is used to simplify the analysis. First, we prove that the variances of the GMPID definitely converge to the mean square error of minimum mean square error (mmse) detection. Second, we derive two sufficient conditions that make the means of the GMPID converge to those of the mmse detection. However, the means of GMPID may not converge when    $ K/M\geq (\sqrt {2}-1)^{2}$   . Therefore, a modified GMPID called scale-and-add GMPID, which converges to the mmse detection in mean and variance for any    $K  , and has a faster convergence speed than the GMPID, but has no higher complexity than the GMPID, is proposed. Finally, numerical results are provided to verify the validity and accuracy of the theoretical results.

Includes members:
  Member "low complexity near optimal signal detection for uplink large scale mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal hybrid analog and digital precoding for downlink mmwave massive mimo systems")
  Member "large system analysis of cognitive radio network via partially projected regularized zero forcing precoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient downlink transmission for multicell massive das with pilot contamination")
  Member "large scale mimo detection for 3gpp lte algorithms and fpga implementations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "energy efficient sensing and communication of parallel gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian message passing for overloaded massive mimo noma")
  Member "par aware large scale multi user mimo ofdm downlink":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic metasurface antennas for uplink massive mimo systems")
  Member "energy and spectral efficiency of very large multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "fixing convergence of gaussian belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressive hyperspectral imaging via approximate message passing")

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)