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: "beam codebook design for 5g mmwave terminals"
  Abstract: A beam codebook of 5G millimeter wave (mmWave) for data communication consists of multiple high-peak-gain beams to compensate the high pathloss at the mmWave bands. These beams also have to point to different angular directions, such that by performing beam searching over the codebook, a good mmWave signal coverage over the full sphere around the terminal (spherical coverage) can be achieved. A model-based beam codebook design that assumes ideal omni-directional antenna pattern, and neglects the impact of terminal housing around the antenna, does not work well because the radiation pattern of a practical mmWave antenna combined with the impact of terminal housing is highly irregular. In this paper, we propose a novel and efficient data-driven method to generate a beam codebook to boost the spherical coverage of mmWave terminals. The method takes as inputs the measured or simulated electric field response data of each antenna and provides the codebook according to the requirements on the codebook size, spherical coverage, and so on. The method can be applied in a straightforward manner to different antenna type, antenna array configuration, placement, and terminal housing design. Our simulation results show that the proposed method generates a codebook better than the benchmark and 802.15.3c codebooks in terms of the spherical coverage.

Connected through hyperedge HE-1 (induced by source paper "learning beam codebooks with neural networks towards environment aware mmwave mimo"):
  with members:
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "deep learning for mmwave beam and blockage prediction using sub 6ghz channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "deep complex networks"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-2 (induced by source paper "deep learning predictive band switching in wireless networks"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "millimeter wave beam selection using out of band spatial information"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-3 (induced by source paper "grip aware analog mmwave beam codebook adaptation for 5g mobile handsets"):
  with members:
    - "statistical blockage modeling and robustness of beamforming in millimeter wave 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)