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: "hybrid beamforming for massive mimo backhaul working title"
  Abstract: The uplink where both the transmitter and receiver can use a large antenna array is considered. This is proposed as a method of antenna offloading and connecting small cell access points (SCAP) in a Two-Tier cellular network. Due to having a limited number of RF-chains, hybrid beamformers are designed where phase-only processing is done at the RF-band, followed by digital processing at the baseband. The proposed receiver is a row combiner that clusters sufficiently correlated antenna elements, and its' performance is compared against random projection via a Discrete Fourier Transform (DFT) matrix. The analogue to the row combiner is a column spreader, which is dependent on the transmit correlation, and repeats the transmitted signal over antenna elements that are correlated. A key benefit of this approach is to reduce the number of phase shifters used, while outperforming the DFT scheme. When only the transmitter has correlation and is RF-chain limited, the baseband precoding vectors are shown to be the eigenvectors of the effective transmit correlation matrix. Depending on the channel correlation, this matrix can be approximated to have a tridiagonal Toeplitz structure with the proposed column spreader (CS). The resulting eigenvalues have a closed form solution which allows us to characterize the sum rate of the system. Most interestingly, the associated eigenvectors do not require knowledge of the effective transmit correlation matrix to be calculated using an Eigenvalue Decomposition (EVD) method.

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)