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: "spatial lobes division based low complexity hybrid precoding and diversity combining for mmwave iot systems"
  Abstract: This paper focuses on the design of low complexity hybrid analog/digital precoding and diversity combining in millimeter wave (mmWave) Internet of Things (IoT) systems. First, by exploiting the sparseness property of the mmWave in the angular domain, we propose a spatial lobes division (SLD) to group the total paths of the mmWave channel into several spatial lobes (SLs), where the paths in each SLs form a low-rank subchannel. Second, based on the SLD operation, we propose a low complexity hybrid precoding scheme, named hybrid precoding based on SLD (HYP-SLD). Specifically, for each low-rank subchannel, we formulate the hybrid precoding design as a sparse reconstruction problem and separately maximizes the spectral efficiency. Finally, we further propose a maximum ratio combining-based diversity combining scheme, named HYP-SLD-MRC, to improve the bit error rate (BER) performance of mmWave IoT systems. Simulation results demonstrate that, the proposed HYP-SLD scheme significantly reduces the complexity of the classic orthogonal matching pursuit scheme. Moreover, the proposed HYP-SLD-MRC scheme achieves great improvement in BER performance compared with the fully digital precoding scheme.

Connected through hyperedge HE-1 (induced by source paper "millimeter wave massive mimo systems relying on generalized sub array connected hybrid precoding"):
  with members:
    - "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems"
    - "on the energy efficiency of mimo hybrid beamforming for millimeter wave systems with nonlinear power amplifiers"
    - "stepwise transmit antenna selection in downlink massive multiuser mimo"
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "codebook based hybrid precoding for millimeter wave multiuser systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-2 (induced by source paper "energy efficient extended sub connected architecture for hybrid precoding in millimeter wave massive mimo systems"):
  with members:
    - "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems"
    - "stepwise transmit antenna selection in downlink massive multiuser mimo"
    - "downlink precoding with mixed statistical and imperfect instantaneous csi for massive mimo systems"
    - "pilot power allocation through user grouping in multi cell massive mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "dynamic subarrays for hybrid precoding in wideband mmwave mimo 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)