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: "per antenna constant envelope precoding for large multi user mimo systems"
  Abstract: We consider the multi-user MIMO broadcast channel with $M$ single-antenna users and $N$ transmit antennas under the constraint that each antenna emits signals having constant envelope (CE). The motivation for this is that CE signals facilitate the use of power-efficient RF power amplifiers. Analytical and numerical results show that, under certain mild conditions on the channel gains, for a fixed $M$, array gain is achievable even under the stringent per-antenna CE constraint (essentially, for a fixed $M$, at sufficiently large $N$ the total transmitted power can be reduced with increasing $N$ while maintaining a fixed information rate to each user). Simulations for the i.i.d. Rayleigh fading channel show that the total transmit power can be reduced linearly with increasing $N$ (i.e., an O(N) array gain). We also propose a precoding scheme which finds near-optimal CE signals to be transmitted, and has O(MN) complexity. Also, in terms of the total transmit power required to achieve a fixed desired information sum-rate, despite the stringent per-antenna CE constraint, the proposed CE precoding scheme performs close to the sum-capacity achieving scheme for an average-only total transmit power constrained channel.

Connected through hyperedge HE-1 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "learning to detect"
    - "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"

Connected through hyperedge HE-2 (induced by source paper "dynamic metasurface antennas for uplink massive mimo systems"):
  with members:
    - "hardware limited task based quantization"
    - "on the spectral efficiency of noncooperative uplink massive mimo systems"
    - "measurement matrix design for phase retrieval based on mutual information"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "resolution adaptive hybrid mimo architectures for millimeter wave communications"
    - "hybrid analog digital beamforming for massive mimo systems"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"

Connected through hyperedge HE-3 (induced by source paper "optimal precoding for multiuser mimo systems with phase quantization and psk modulation via branch and bound"):
  with members:
    - "quantized constant envelope precoding with psk and qam signaling"
    - "mse optimal 1 bit precoding for multiuser mimo via branch and bound"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "minimum ber precoding in 1 bit massive mimo systems"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"

Connected through hyperedge HE-4 (induced by source paper "quantized constant envelope precoding with psk and qam signaling"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "quantized precoding for multi antenna downlink channels with magiq"
    - "nonlinear precoding for phase quantized constant envelope massive mu mimo ofdm"
    - "linear precoding with low resolution dacs for massive mu mimo ofdm downlink"
    - "psk precoding in multi user miso systems"
    - "massive mimo downlink 1 bit precoding with linear programming for psk signaling"
    - "an efficient manifold algorithm for constructive interference based constant envelope precoding"

Connected through hyperedge HE-5 (induced by source paper "glse precoders for massive mimo systems analysis and applications"):
  with members:
    - "stepwise transmit antenna selection in downlink massive multiuser mimo"
    - "nonlinear precoders for massive mimo systems with general constraints"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "asymptotics of transmit antenna selection impact of multiple receive antennas"
    - "energy efficient coordinated multi cell multi group multicast beamforming with antenna selection"
    - "a new class of nonlinear precoders for hardware efficient massive mimo systems"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-6 (induced by source paper "one bit precoding and constellation range design for massive mimo with qam signaling"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "massive mimo downlink 1 bit precoding with linear programming for psk signaling"
    - "an efficient manifold algorithm for constructive interference based constant envelope precoding"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "analysis of one bit quantized precoding for the multiuser massive mimo downlink"
    - "energy efficient symbol level precoding in multiuser miso based on relaxed detection region"

Connected through hyperedge HE-7 (induced by source paper "a framework for one bit and constant envelope precoding over multiuser massive miso channels"):
  with members:
    - "one bit massive mimo precoding via a minimum symbol error probability design"
    - "minimum symbol error rate based constant envelope precoding for multiuser massive miso downlink"
    - "quantized constant envelope precoding with psk and qam signaling"
    - "multiuser one bit massive mimo precoding under mpsk signaling"
    - "mse optimal 1 bit precoding for multiuser mimo via branch and bound"
    - "one bit precoding and constellation range design for massive mimo with qam signaling"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "nonlinear precoding for phase quantized constant envelope massive mu mimo ofdm"

Connected through hyperedge HE-8 (induced by source paper "on robustness of massive mimo systems against passive eavesdropping under antenna selection"):
  with members:
    - "stepwise transmit antenna selection in downlink massive multiuser mimo"
    - "iterative antenna selection for secrecy enhancement in massive mimo wiretap channels"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "asymptotics of transmit antenna selection impact of multiple receive antennas"
    - "asymptotic performance analysis of spatially reconfigurable antenna arrays"
    - "optimal number of transmit antennas for secrecy enhancement in massive mimome channels"
    - "secrecy and energy efficiency in massive mimo aided heterogeneous c ran a new look at interference"
    - "on secrecy performance of antenna selection aided mimo systems against eavesdropping"

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)