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: "channel estimation method and phase shift design for reconfigurable intelligent surface assisted mimo networks"
  Abstract: Reconfigurable intelligent surface (RIS) aided multiple-input multiple-output (MIMO) communication is becoming a serious contender for future wireless networks. The reason for this attention is due to reliable communication and low cost deployment offered by the RIS based communication systems compared to the conventional massive MIMO systems. However, the performance of an RIS assisted MIMO system heavily depends on the quality of channel state information available at both ends. As communicating devices and RIS are in close proximity, the desired channels can be modeled as line-of-sight (LOS) channels, where dominant components comes from LOS paths. Thus, leading to ill-conditioned channel matrices. To estimate these channel matrices with high quality, we propose a two stage channel estimation method for RIS aided MIMO time-division duplexing (TDD) communication systems. In particular, we employ the conventional TDD based MIMO channel estimation technique in the first stage to estimate the direct MIMO channel between terminals with no RIS. Whereas, in the second stage of the channel estimation process, we propose to use a recently developed bilinear adaptive vector approximate message passing (BAdVAMP) algorithm to estimate ill-conditioned RIS channels. The BAdVAMP method belongs to the class of approximate message passing (AMP) algorithms and it is used in wide range of estimation and learning problems. We also propose a phase shift design for the RIS using the estimated channels. Specifically, we formulate an optimization problem that maximizes the total channel gain at the user. A closed-form expression to obtain the phase shift of each passive element in the RIS is also derived. Numerical results show that the proposed BAdVAMP based RIS channel estimation performs better than its counterpart i.e., bilinear generalized AMP (BiGAMP) based RIS channel estimation scheme.

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"

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)