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: "5g millimeter wave cellular system capacity with fully digital beamforming"
  Abstract: Due to heavy reliance of millimeter-wave (mmWave) wireless systems on directional links, Beamforming (BF) with high-dimensional arrays is essential for cellular systems in these frequencies. How to perform the array processing in a power efficient manner is a fundamental challenge. Analog and hybrid BF require fewer analog-to-digital converters (ADCs), but can only communicate in a small number of directions at a time,limiting directional search, spatial multiplexing and control signaling. Digital BF enables flexible spatial processing, but must be operated at a low quantization resolution to stay within reasonable power levels. This paper presents a simple additive white Gaussian noise (AWGN) model to assess the effect of low resolution quantization of cellular system capacity. Simulations with this model reveal that at moderate resolutions (3-4 bits per ADC), there is negligible loss in downlink cellular capacity from quantization. In essence, the low-resolution ADCs limit the high SNR, where cellular systems typically do not operate. The findings suggest that low-resolution fully digital BF architectures can be power efficient, offer greatly enhanced control plane functionality and comparable data plane performance to analog BF.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on beam management for 3gpp nr at mmwave frequencies"):
  with members:
    - "statistical blockage modeling and robustness of beamforming in millimeter wave systems"
    - "adaptive beamwidth selection for contention based access periods in millimeter wave wlans"
    - "standalone and non standalone beam management for 3gpp nr at mmwaves"
    - "study of realistic antenna patterns in 5g mmwave cellular scenarios"
    - "initial access frameworks for 3gpp nr at mmwave frequencies"
    - "on the beamformed broadcast signaling for millimeter wave cell discovery performance analysis and design insight"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"

Connected through hyperedge HE-2 (induced by source paper "a case for digital beamforming at mmwave"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "low power analog to digital conversion in millimeter wave systems impact of resolution and bandwidth on performance"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"

Connected through hyperedge HE-3 (induced by source paper "initial access frameworks for 3gpp nr at mmwave frequencies"):
  with members:
    - "adaptive beamwidth selection for contention based access periods in millimeter wave wlans"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "improved handover through dual connectivity in 5g mmwave mobile networks"
    - "user association in 5g networks a survey and an outlook"
    - "initial access in 5g mmwave cellular networks"
    - "directional cell discovery in millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"
    - "millimeter wave channel modeling and cellular capacity evaluation"

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)