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: "progressive channel estimation and passive beamforming for intelligent reflecting surface with discrete phase shifts"
  Abstract: Intelligent reflecting surface (IRS) has emerged as a promising technology to engineer the radio signal propagation for catering to wireless communication, by leveraging a massive number of low-cost passive reflecting elements. Prior studies on IRS have mostly assumed perfect channel state information (CSI) available for designing the IRS passive beamforming as well as the continuously adjustable phase shift at each of its reflecting elements, which, however, have simplified two challenging issues for implementing IRS in practice, namely, its channel estimation and passive beamforming designs both under the constraint of discrete phase shifts. To address these issues, we consider in this paper an IRS-aided single-user communication system with discrete phase shifts and propose a new joint design framework for progressive IRS channel estimation and passive beamforming. Specifically, we consider the practical block-based transmission, where each block has a finite number of training symbols for channel estimation. However, different from the conventional "all-at-once" channel estimation, i.e., the channels of all IRS elements are estimated at one time which inevitably causes long delay for data transmission, we propose a novel hierarchical training reflection design for IRS such that by properly partitioning its reflecting elements into groups/subgroups and assigning each group/subgroup of elements differently-combined discrete phase shifts over multiple blocks, all IRS elements' channels can be efficiently resolved even if there is only a small (insufficient) number of training symbols per block. Based on the resolved IRS channels in each block, we further design the progressive passive beamforming at the IRS with discrete phase shifts to improve the achievable rate for data transmission over the blocks.

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"
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"

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)