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 "out of band radiation from large antenna arrays"):
  Abstract: Co-existing wireless systems, which share a common spectrum, need to mitigate out-of-band (OOB) radiation to avoid excessive interference. For legacy systems, OOB radiation is well understood and is commonly handled by digital precompensation techniques. When using large arrays, however, new phenomena and hardware limitations have to be considered. First, signals can be radiated directionally, which might focus the OOB radiation. Second, low-complexity hardware is used for cost reasons, which increases the relative amount of OOB radiation. Given that massive MIMO and millimeter wave communication rely on base stations with a large number of antennas, the spatial behavior of OOB radiation from large arrays will have significant implications for the hardware requirements of future base stations. We show that, if the OOB radiation is beamformed, its array gain is never larger than that of the in-band signal. In many cases, the OOB radiation is close to isotropic even when the in-band signal is highly directive. With the same total radiated power, the OOB radiation from large arrays is therefore never more severe than from a legacy system with the same adjacent-channel-leakage ratio. Further, the OOB radiation is less detrimental than from a legacy system since the high array gain of the in-band signal allows large arrays to radiate less total power than legacy systems. We also show how OOB radiation from large arrays varies with location in static propagation environments and how these effects vanish when averaged over the small-scale fading. Since a higher relative amount of OOB radiation can be tolerated for large arrays, the linearity requirement can be relaxed as compared to legacy systems. Specifically, less stringent linearity requirements on each transmitter makes it possible to build large arrays from low-complexity hardware.

Includes members:
  Member "out of band radiation measure for mimo arrays with beamformed transmission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "out of band radiation from antenna arrays clarified")
  Member "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "on the impact of hardware impairments on massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hardware distortion correlation has negligible impact on ul massive mimo spectral efficiency")
  Member "five disruptive technology directions for 5g":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "constant envelope multi user precoding for frequency selective massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fdd massive mimo based on efficient downlink channel reconstruction")

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)