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 "impact of limited feedback on mimo ofdm systems using joint beamforming"):
  Abstract: In multi input multi output antenna systems, beamforming is a technique for guarding against the negative effects of fading. However, this technique requires the transmitter to have perfect knowledge of the channel which is often not available a priori. A solution to overcome this problem is to design the beamforming vector using a limited number of feedback bits sent from the receiver to the transmitter. In the case of limited feedback, the beamforming vector is limited to lie in a codebook that is known to both the transmitter and receiver.When the feedback is strictly limited, important issues are how to quantize the information needed at the transmitter and how much improvement in associated performance can be obtained as a function of the amount of feedback available.In this paper channel quantization schema using simple approach to codebook design (random vector quantization)is illustrated. Performance results show that even with a few bits of feedback, performance can be close to that with perfect channel knowledge at the transmitter.

Includes members:
  Member "optimization of training and feedback overhead for beamforming over block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on optimizing feedback interval for temporally correlated mimo channels with transmit beamforming and finite rate feedback")
  Member "capacity of a multiple antenna fading channel with a quantized precoding matrix":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on optimizing feedback interval for temporally correlated mimo channels with transmit beamforming and finite rate feedback")

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.OH(Other Computer Science)