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: "linear precoding for mimo channels with qam constellations and reduced complexity"
  Abstract: Author(s): Ketseoglou, Thomas; Ayanoglu, Ender | Abstract: In this paper, the problem of designing a linear precoder for Multiple-Input Multiple-Output (MIMO) systems in conjunction with Quadrature Amplitude Modulation (QAM) is addressed. First, a novel and efficient methodology to evaluate the input-output mutual information for a general Multiple-Input Multiple-Output (MIMO) system as well as its corresponding gradients is presented, based on the Gauss-Hermite quadrature rule. Then, the method is exploited in a block coordinate gradient ascent optimization process to determine the globally optimal linear precoder with respect to the MIMO input-output mutual information for QAM systems with relatively moderate MIMO channel sizes. The proposed methodology is next applied in conjunction with the complexity-reducing per-group processing (PGP) technique, which is semi-optimal, to both perfect channel state information at the transmitter (CSIT) as well as statistical channel state information (SCSI) scenarios, with high transmitting and receiving antenna size, and for constellation size up to $M=64$. We show by numerical results that the precoders developed offer significantly better performance than the configuration with no precoder, and the maximum diversity precoder for QAM with constellation sizes $M=16,~32$, and $~64$ and for MIMO channel size $100\times100$.

Connected through hyperedge HE-1 (induced by source paper "low complexity mimo precoding for finite alphabet signals"):
  with members:
    - "secure massive mimo transmission with an active eavesdropper"
    - "transmit designs for the mimo broadcast channel with statistical csi"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "on capacity of large scale mimo multiple access channels with distributed sets of correlated antennas"
    - "precoding by pairing subchannels to increase mimo capacity with discrete input alphabets"
    - "statistical eigenmode transmission over jointly correlated mimo channels"
    - "on optimal precoding in linear vector gaussian channels with arbitrary input distribution"

Connected through hyperedge HE-2 (induced by source paper "low complexity mimo precoding with discrete signals and statistical csi"):
  with members:
    - "secure massive mimo transmission with an active eavesdropper"
    - "linear precoding for the mimo multiple access channel with finite alphabet inputs and statistical csi"
    - "secure massive mimo transmission in the presence of an active eavesdropper"
    - "transmit designs for the mimo broadcast channel with statistical csi"
    - "linear precoder design for mimo interference channels with finite alphabet signaling"
    - "on capacity of large scale mimo multiple access channels with distributed sets of correlated antennas"
    - "precoding by pairing subchannels to increase mimo capacity with discrete input alphabets"
    - "statistical eigenmode transmission over jointly correlated mimo channels"

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)