human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "joint active and passive beamforming optimization for intelligent reflecting surface assisted swipt under qos constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "resource allocation in intelligent reflecting surface assisted noma systems")
  Member "bilinear recovery using adaptive vector amp":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "intelligent reflecting surface with discrete phase shifts channel estimation and passive beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "cascaded channel estimation for large intelligent metasurface assisted massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "hybrid beamforming for reconfigurable intelligent surface based multi user communications achievable rates with limited discrete phase shifts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reflection resource management for intelligent reflecting surface aided wireless networks")
  Member "channel estimation for intelligent reflecting surface assisted multiuser communications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "resource allocation for irs assisted full duplex cognitive radio systems")
  Member "channel estimation and transmission for intelligent reflecting surface assisted thz communications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "channel estimation and hybrid beamforming for reconfigurable intelligent surfaces assisted thz communications")
  Member "reliability analysis of large intelligent surfaces liss rate distribution and outage probability":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training")

Question: Please tell me which class the source paper 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)