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: "reconfigurable intelligent surface empowered 6g wireless communications challenges and opportunities"
  Abstract: Although the commercialization of fifth-generation (5G) mobile networks is just in its initial stage, sixth-generation (6G) research effort has been kicked off from the academic and industrial points of view. Researches on the candidate technologies of 6G, such as ultra-massive multiple-input multiple-output (MIMO), terahertz communications, artificial intelligence (AI) empowered wireless networks, and reconfigurable intelligent surfaces (RISs), are all in full swing. Among them, RISs are regarded as a promising emerging hardware technology to improve the spectrum and energy efficiency of wireless networks by artificially reconfiguring the propagation environment of electromagnetic waves. Due to the unique advantages in enhancing wireless communications, RISs have become a hot topic in recent researches. In this article, we focus on three main challenges of implementing RISs in wireless networks, namely, channel state information acquisition, passive information transfer, and resource allocation of RIS-aided communication systems. We summarize the state-of-the-art solutions and explore potential research directions. Furthermore, we enumerate promising application scenarios of RISs, such as edge intelligence, unmanned-aerial-vehicle communications, and so forth.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for intelligent reflecting surfaces towards standalone operation"):
  with members:
    - "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"
    - "holographic mimo surfaces for 6g wireless networks opportunities challenges and trends"
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"

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)