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: "propagation modeling and analysis of reconfigurable intelligent surfaces for indoor and outdoor applications in 6g wireless systems"
  Abstract: In 5G networks, the enhanced mobile broadband, massive Internet-of-things, and ultra-reliable and low latency communications are defined as three use-cases with diverse requirements. To cope with these objectives, more radical physical layer concepts are needed to comprehend the potential requirements in future wireless systems. This has pushed researchers to look into new paradigms beyond 5G and 6G wireless systems are conceptualized in recent years. One of the promising technologies is the so-called reconfigurable intelligent surfaces (RISs) which convert the wireless channel into a transmit entity by manipulating the impinging waves using artificial elements. In this paper, the potential benefits of using RISs are investigated for indoor/outdoor setups and various frequency bands. Many RIS-assisted communication scenarios are studied to ensure reliable transmission in deteriorated channels. First, a general propagation model with a single RIS is considered and the effect of the total number of reflecting elements on the distribution of the received SNR and error performance is investigated. Also, the path loss exponent is derived for below and above 6 GHz by considering empirical path loss models. Furthermore, propagation models with multiple RISs are developed and analyzed for indoor and outdoor NLOS scenarios. The bit error performance is evaluated and the relation between error performance and the number of reflecting elements of RISs is determined. RIS selection strategies are also investigated for systems equipped with multiple RISs. Through extensive simulations, it is demonstrated that the RIS-assisted systems provide promising solutions for indoor/outdoor scenarios and exhibit significant results in error performance and achievable data rates even in the presence of system imperfections such as limited range phase adjustment and imperfect channel phase estimation at RISs.

Connected through hyperedge HE-1 (induced by source paper "power scaling laws and near field behaviors of massive mimo and intelligent reflecting surfaces"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "spatially stationary model for holographic mimo small scale fading"
    - "beyond max snr joint encoding for reconfigurable intelligent surfaces"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "reconfigurable intelligent surfaces bridging the gap between scattering and reflection"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"

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)