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 "fronthaul limited uplink ofdma in ultra dense cran with hybrid decoding"):
  Abstract: In an ultra-dense cloud radio access network (UD-CRAN), a large number of remote radio heads (RRHs), typically employed as simple relay nodes, are distributed in a target area, which could even outnumber their served users. However, one major challenge is that the large amount of information required to be transferred between each RRH and the central processor (CP) for joint signal processing can easily exceed the capacity of the fronthaul links connecting them. This motivates our study in this paper on a new  hybrid  decoding scheme where in addition to quantizing and forwarding the received signals for joint decoding at the CP, i.e.,   forward-and-decode (FaD)  as in the conventional CRAN, the RRHs can locally   decode-and-forward (DaF)  the user messages to save the fronthaul capacity. In particular, we consider the uplink transmission in an orthogonal frequency division multiple access (OFDMA)-based UD-CRAN, where the proposed hybrid decoding is performed on each OFDMA sub-channel (SC). We study a joint optimization of the processing mode selections (DaF or FaD), user-SC assignments and the users’ transmit power allocations over all SCs to maximize their weighted-sum-rate subject to the RRHs’ individual fronthaul capacity constraints and the users’ individual power constraints. Although the problem is nonconvex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. Further, we propose a low-complexity greedy algorithm which is shown to achieve close to the optimal performance under practical setups. Simulation results show the promising throughput gains of the proposed designs with hybrid decoding, compared to the existing schemes that perform either DaF or FaD at all SCs.

Includes members:
  Member "cross layer design for downlink multihop cloud radio access networks with network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interference mitigation via rate splitting and common message decoding in cloud radio access networks")
  Member "joint millimeter wave fronthaul and ofdma resource allocation in ultra dense cran":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint beamforming and power splitting design for c ran with multicast fronthaul")
  Member "recent advances in cloud radio access networks system architectures key techniques and open issues":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "content centric sparse multicast beamforming for cache enabled cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "green ofdma resource allocation in cache enabled cran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative multi bitrate video caching and transcoding in multicarrier noma assisted heterogeneous virtualized mec networks")
  Member "wireless communications in the era of big data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges")
  Member "large scale convex optimization for ultra dense cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "optimized uplink transmission in multi antenna c ran with spatial compression and forward":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "computation offloading for iot in c ran optimization and deep learning")

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)