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: "power efficient irs assisted noma"
  Abstract: In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the capacity region when the channels are quasi-degraded. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

Connected through hyperedge HE-1 (induced by source paper "resource allocation in intelligent reflecting surface assisted noma systems"):
  with members:
    - "joint active and passive beamforming optimization for intelligent reflecting surface assisted swipt under qos constraints"
    - "capacity characterization for intelligent reflecting surface aided mimo communication"
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"
    - "intelligent reflecting surface assisted non orthogonal multiple access"
    - "intelligent reflecting surface design for mimo system by maximizing sum path gains"
    - "a simple design of irs noma transmission"
    - "towards smart radio environment for wireless communications via intelligent reflecting surfaces a comprehensive survey"
    - "joint beamforming design in multi cluster miso noma intelligent reflecting surface aided downlink communication networks"

Connected through hyperedge HE-2 (induced by source paper "capacity and optimal resource allocation for irs assisted multi user communication systems"):
  with members:
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "robust and secure wireless communications via intelligent reflecting surfaces"
    - "capacity characterization for intelligent reflecting surface aided mimo communication"
    - "intelligent reflecting surface assisted non orthogonal multiple access"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "a simple design of irs noma transmission"
    - "irs enhanced ofdma joint resource allocation and passive beamforming optimization"
    - "mimo assisted networks relying on large intelligent surfaces a stochastic geometry model"

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)