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: "irs enhanced ofdma joint resource allocation and passive beamforming optimization"
  Abstract: Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless communication spectral efficiency with low hardware and energy cost. In this letter, we consider the integration of IRS to an orthogonal frequency division multiple access (OFDMA) based multiuser downlink communication system, and study the pertinent joint optimization of the IRS reflection coefficients and OFDMA time-frequency resource block as well as power allocations to maximize the users' common (minimum) rate. Specifically, due to the lack of frequency-selective passive beamforming capability at the IRS, only one set of reflection coefficients can be designed for adapting to a large number of channels of multiple users over different frequency sub-bands. To tackle this difficulty, we propose a novel dynamic passive beamforming scheme where the IRS reflection coefficients are dynamically adjusted over different time slots within each channel coherence block to create artificial time-varying channels and select only a subset of the users to be simultaneously served in each time slot, thus achieving a higher passive beamforming gain. Although the formulated optimization problem is non-convex, we propose an efficient algorithm to obtain a suboptimal solution to it. Numerical results show that the proposed scheme significantly improves the system common rate over the setup without IRS and that with random IRS reflection coefficients. Moreover, our proposed dynamic passive beamforming outperforms the fixed passive beamforming which employs a common set of reflection coefficients in each channel coherence block, by more flexibly balancing between passive beamforming and multiuser diversity gains.

Connected through hyperedge HE-1 (induced by source paper "intelligent reflecting surface assisted multi user ofdma channel estimation and training design"):
  with members:
    - "intelligent reflecting surface assisted multiple access with user pairing noma or oma"
    - "parallel factor decomposition channel estimation in ris assisted multi user miso communication"
    - "intelligent reflecting surface with discrete phase shifts channel estimation and passive beamforming"
    - "secure wireless communication via intelligent reflecting surface"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "channel estimation for intelligent reflecting surface assisted multiuser communications framework algorithms and analysis"
    - "capacity characterization for intelligent reflecting surface aided mimo communication"
    - "progressive channel estimation and passive beamforming for intelligent reflecting surface with discrete phase shifts"

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"
    - "power efficient irs assisted noma"
    - "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"
    - "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)