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 "a time variant channel prediction and feedback framework for interference alignment"):
  Abstract: In interference channels, channel state information (CSI) can be exploited to reduce the interference signal dimensions and thus achieve the optimal capacity scaling, i.e. degrees of freedom, promised by the interference alignment technique. However, imperfect CSI, due to channel estimation error, imperfect CSI feedback and time selectivity of the channel, lead to a performance loss. In this work, we propose a novel limited feedback algorithm for single-input single-output interference alignment in time-variant channels. The feedback algorithm encodes the channel evolution in a small number of subspace coefficients, which allow for reduced-rank channel prediction to compensate for the channel estimation error due to time selectivity of the fading process and feedback delay. An upper bound for the rate loss caused by feedback quantization and channel prediction is derived. Based on this bound, we develop a dimension switching algorithm for the reduced-rank predictor to find the best tradeoff between quantization- and prediction-error. Besides, we characterize the scaling of the required number of feedback bits in order to decouple the rate loss due to channel quantization from the transmit power. Simulation results show that a rate gain over the traditional non-predictive feedback strategy can be secured and a 60% higher rate is achieved at 20 dB signal-to-noise ratio with moderate mobility.

Includes members:
  Member "on the degrees of freedom for opportunistic interference alignment with 1 bit feedback the 3 cell case":
    also connected through other hyperedges:
      (none)
  Member "delay and doppler spreads of nonstationary vehicular channels for safety relevant scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward intelligent vehicular networks a machine learning framework")
  Member "grassmannian differential limited feedback for interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online learning models for content popularity prediction in wireless edge caching")
  Member "adaptive interference alignment with csi uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends")
  Member "ensemble properties of rvq based limited feedback beamforming codebooks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "clustering based codebook design for mimo communication system")
  Member "optimization of training and feedback overhead for beamforming over block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on optimizing feedback interval for temporally correlated mimo channels with transmit beamforming and finite rate feedback")
  Member "interference alignment with analog channel state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the feasibility of interference alignment in compounded mimo broadcast channels with antenna correlation and mixed user classes")
  Member "interference alignment under limited feedback for mimo interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "opportunistic downlink interference alignment for multi cell mimo networks")

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)