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 "cloud radio access networks uplink channel estimation and downlink precoding"):
  Abstract: The gains afforded by cloud radio access network (C-RAN) in terms of savings in capital and operating expenses, flexibility, interference management and network densification rely on the presence of high-capacity low-latency fronthaul connectivity between remote radio heads (RRHs) and baseband unit (BBU). In light of the non-uniform and limited availability of fiber optics cables, the bandwidth constraints on the fronthaul network call, on the one hand, for the development of advanced baseband compression strategies and, on the other hand, for a closer investigation of the optimal functional split between RRHs and BBU. In this chapter, after a brief introduction to signal processing challenges in C-RAN, this optimal function split is studied at the physical (PHY) layer as it pertains to two key baseband signal processing steps, namely channel estimation in the uplink and channel encoding/ linear precoding in the downlink. Joint optimization of baseband fronthaul compression and of baseband signal processing is tackled under different PHY functional splits, whereby uplink channel estimation and downlink channel encoding/ linear precoding are carried out either at the RRHs or at the BBU. The analysis, based on information-theoretical arguments, and numerical results yields insight into the configurations of network architecture and fronthaul capacities in which different functional splits are advantageous. The treatment also emphasizes the versatility of deterministic and stochastic successive convex approximation strategies for the optimization of C-RANs.

Includes members:
  Member "fronthaul compression and precoding design for c rans over ergodic fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks")
  Member "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")
  Member "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "caching at base stations with multi cluster multicast wireless backhaul via accelerated first order algorithm")
  Member "optimal channel training in uplink network mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uplink channel estimation and data transmission in millimeter wave cran with lens antenna arrays")

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)