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 "resource allocation optimization for delay sensitive traffic in fronthaul constrained cloud radio access networks"):
  Abstract: The cloud radio access network (C-RAN) provides high spectral and energy efficiency performances, low expenditures and intelligent centralized system structures to operators, which has attracted intense interests in both academia and industry. In this paper, a hybrid coordinated multi-point transmission (H-CoMP) scheme is designed for the downlink transmission in C-RANs, which fulfills the flexible tradeoff between cooperation gain and fronthaul consumption. The queue-aware power and rate allocation with constraints of average fronthaul consumption for the delay-sensitive traffic are formulated as an infinite horizon constrained partially observed Markov decision process (POMDP), which takes both the urgent queue state information (QSI) and the imperfect channel state information at transmitters (CSIT) into account. To deal with the curse of dimensionality involved with the equivalent Bellman equation, the linear approximation of post-decision value functions is utilized. A stochastic gradient algorithm is presented to allocate the queue-aware power and transmission rate with H-CoMP, which is robust against unpredicted traffic arrivals and uncertainties caused by the imperfect CSIT. Furthermore, to substantially reduce the computing complexity, an online learning algorithm is proposed to estimate the per-queue post-decision value functions and update the Lagrange multipliers. The simulation results demonstrate performance gains of the proposed stochastic gradient algorithms, and confirm the asymptotical convergence of the proposed online learning algorithm.

Includes members:
  Member "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "backhaul limited asymmetric cooperation for mimo cellular networks via semidefinite relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cloud edge coordinated processing low latency multicasting transmission")
  Member "ergodic capacity analysis of remote radio head associations in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "channel matrix sparsity with imperfect channel state information in cloud radio access networks")
  Member "delay aware bs discontinuous transmission control and user scheduling for energy harvesting downlink coordinated mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimum age of information in the internet of things with non uniform status packet sizes")
  Member "a survey on delay aware resource control for wireless systems large deviation theory stochastic lyapunov drift and distributed stochastic learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "queue aware dynamic clustering and power allocation for network mimo systems via distributed stochastic learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic resource allocation in co located and cell free massive mimo")
  Member "optimized data sharing in multicell mimo with finite backhaul capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interference mitigation via rate splitting and common message decoding in cloud radio access 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)