human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "uplink channel estimation and data transmission in millimeter wave cran with lens antenna arrays"):
  Abstract: Millimeter-wave (mmWave) communication and network densification hold great promise for achieving high-rate communication in next-generation wireless networks. Cloud radio access network (CRAN), in which low-complexity remote radio heads (RRHs) coordinated by a central unit (CU) are deployed to serve users in a distributed manner, is a cost-effective solution to achieve network densification. However, when operating over a large bandwidth in the mmWave frequencies, the digital fronthaul links in a CRAN would be easily saturated by the large amount of sampled and quantized signals to be transferred between the RRHs and the CU. To tackle this challenge, we propose in this paper a new architecture for the mmWave-based CRAN with advanced lens antenna arrays at the RRHs. Due to the energy focusing property, the lens antenna arrays are effective in exploiting the angular sparsity of mmWave channels, and thus help in substantially reducing the fronthaul rate and simplifying the signal processing at the multi-antenna RRHs and the CU, even when the channels are frequency-selective. We consider the uplink transmission in a mmWave CRAN with lens antenna arrays and propose a low-complexity quantization bit allocation scheme for multiple antennas at each RRH to meet the given fronthaul rate constraint. Furthermore, we propose a channel estimation technique that exploits the energy focusing property of the lens array and can be implemented at the CU with low complexity. Finally, we compare the proposed mmWave CRAN using lens antenna arrays with a conventional CRAN using uniform planar arrays at the RRHs, and show that the proposed design achieves significant throughput gains, yet with much lower complexity.

Includes members:
  Member "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint uplink and downlink transmissions in user centric ofdma cloud ran")
  Member "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint beamforming and power splitting design for c ran with multicast fronthaul")
  Member "multi user millimeter wave mimo with full dimensional lens antenna array":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligent reflecting and transmitting surface aided millimeter wave massive mimo")
  Member "rf lens embedded massive mimo systems fabrication issues and codebook design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "channel estimation for hybrid architecture based wideband millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "cost effective millimeter wave communications with lens antenna array":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatial lobes division based low complexity hybrid precoding and diversity combining for mmwave iot systems")
  Member "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "optimized uplink transmission in multi antenna c ran with spatial compression and forward":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "computation offloading for iot in c ran optimization and deep learning")

Question: Please tell me which class the source paper 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)