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 "joint optimization of fronthaul compression and bandwidth allocation in uplink h cran with large system analysis"):
  Abstract: In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner–Ziv coding. Different decoding strategies are also considered. This paper aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue, because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach’s algorithm is proposed to find the optimal solution to the approximate problem. In summary, this paper provides an economic solution to the challenge of constrained fronthaul capacity and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.

Includes members:
  Member "message passing in c ran joint user activity and signal detection":
    also connected through other hyperedges:
      (none)
  Member "downlink energy efficiency of power allocation and wireless backhaul bandwidth allocation in heterogeneous small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy and spectral efficiency tradeoff in ofdma networks via antenna selection strategy")
  Member "fronthaul compression and transmit beamforming optimization for multi antenna uplink c ran":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "gaussian message passing iterative detection for mimo noma systems with massive access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior")
  Member "contract based interference coordination in heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks")
  Member "system architecture and key technologies for 5g heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "cloud radio access network virtualizing wireless access for dense heterogeneous systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "energy efficient design of mimo heterogeneous networks with wireless backhaul":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense 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)