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 "intelligent reflecting surface assisted multi user ofdma channel estimation and training design"):
  Abstract: To achieve the full passive beamforming gains of intelligent reflecting surface (IRS), accurate channel state information (CSI) is indispensable but practically challenging to acquire, due to the excessive amount of channel parameters to be estimated which increases with the number of IRS reflecting elements as well as that of IRS-served users. To tackle this challenge, we propose in this paper two efficient channel estimation schemes for different channel setups in an IRS-assisted multi-user broadband communication system employing the orthogonal frequency division multiple access (OFDMA). The first channel estimation scheme, which estimates the CSI of all users in parallel simultaneously at the access point (AP), is applicable for arbitrary frequency-selective fading channels. In contrast, the second channel estimation scheme, which exploits a key property that all users share the same (common) IRS-AP channel to enhance the training efficiency and support more users, is proposed for the typical scenario with line-of-sight (LoS) dominant user-IRS channels. For the two proposed channel estimation schemes, we further optimize their corresponding training designs (including pilot tone allocations for all users and IRS time-varying reflection pattern) to minimize the channel estimation error. Moreover, we derive and compare the fundamental limits on the minimum training overhead and the maximum number of supportable users of these two schemes. Simulation results verify the effectiveness of the proposed channel estimation schemes and training designs, and show their significant performance improvement over various benchmark schemes.

Includes members:
  Member "intelligent reflecting surface assisted multiple access with user pairing noma or oma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")
  Member "parallel factor decomposition channel estimation in ris assisted multi user miso communication":
    also connected through other hyperedges:
      (none)
  Member "intelligent reflecting surface with discrete phase shifts channel estimation and passive beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "secure wireless communication via intelligent reflecting surface":
    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 "channel estimation for intelligent reflecting surface assisted multiuser communications framework algorithms and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligent reflecting surface assisted massive mimo communications")
  Member "capacity characterization for intelligent reflecting surface aided mimo communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")
  Member "progressive channel estimation and passive beamforming for intelligent reflecting surface with discrete phase shifts":
    also connected through other hyperedges:
      (none)

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)