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 "broadcast channels with delayed finite rate feedback predict or observe"):
  Abstract: Most multiuser precoding techniques require accurate transmitter channel state information (CSIT) to maintain orthogonality between the users. Such techniques have proven quite fragile in time-varying channels because the CSIT is inherently imperfect due to estimation and feedback delay, as well quantization noise. An alternative approach recently proposed by Maddah-Ali and Tse (MAT) allows for significant multiplexing gain in the multi-input single-output (MISO) broadcast channel (BC) even with transmit CSIT that is completely stale, i.e. uncorrelated with the current channel state. With $K$ users, their scheme claims to lose only a $\log(K)$ factor relative to the full $K$ degrees of freedom (DoF) attainable in the MISO BC with perfect CSIT for large $K$. However, their result does not consider the cost of the feedback, which is potentially very large in high mobility (short channel coherence time). In this paper, we more closely examine the MAT scheme and compare its DoF gain to single user transmission (which always achieves 1 DoF) and partial CSIT linear precoding (which achieves up to $K$). In particular, assuming the channel coherence time is $N$ symbol periods and the feedback delay is $N_{\rm fd}$ we show that when $N   (1+o(1)) (N_{\rm fd}+ K / \log K)(1-\log^{-1}K)^{-1}$ (long coherence time), zero-forcing precoding outperforms the other two. The MAT scheme is optimal for intermediate coherence times, which for practical parameter choices is indeed quite a large and significant range, even accounting for the feedback cost.

Includes members:
  Member "multi mode transmission for the mimo broadcast channel with imperfect channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provisioning energy efficiency and qos for multi carrier comp with limited feedback")
  Member "completely stale transmitter channel state information is still very useful":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "achievable dof regions of three user mimo broadcast channel with delayed csit")
  Member "retrospective interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "throughput region of spatially correlated interference packet networks")
  Member "limited feedback based block diagonalization for the mimo broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mimo amplify and forward precoding for networked control systems")
  Member "space division multiple access with a sum feedback rate constraint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling analysis and optimization of caching in multi antenna small cell 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)