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 "power and rate adaptation improves the effective capacity of c ran for nakagami m fading channels"):
  Abstract: We propose a quality-of-service (QoS) driven power-and rate-adaptation scheme for wireless cloud radio access networks (C-RANs), where each radio remote head (RRH) is connected to the baseband unit (BBU) pool through high-speed optical links. The RRHs jointly support the users by efficiently exploiting the enhanced spatial degrees of freedom attained by the powerful cloud computing facilitated by the BBU pool. Our proposed scheme aims for maximizing the effective capacity (EC) of the user subject to both per-RRH average-and peak-power constraints, where the EC is defined as the tele-traffic maximum arrival rate that can be supported by the C-RAN under the statistical delay-QoS requirement. We first transform the EC maximization problem into an equivalent convex optimization problem. By using the Lagrange dual decomposition method and satisfying the Karush-Kuhn-Tucker (KKT) conditions, the optimal transmission power of each RRH can be obtained in closed form. Furthermore, an online tracking method is provided for approximating the average power of each RRH for the sake of updating the Lagrange dual variables. For the special case of two RRHs, the expression of the average power to be assigned to each RRH can be calculated in explicit form, which can be numerically evaluated. Hence, the Lagrange dual variables can be computed in advance in this special case. Furthermore, we derive the power allocation for two important extreme cases: 1) no delay constraint; 2) extremely stringent delay-requirements. Additionally, we also extend our work to the other two important problems: 1) Energy efficiency maximization problem; 2) EC maximization for a multiuser case. Our simulation results show that the proposed scheme significantly outperforms the conventional algorithm without considering the delay requirements.

Includes members:
  Member "joint precoding and rrh selection for user centric green mimo c ran":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid precoder design for cache enabled millimeter wave 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 "what will 5g be":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "ergodic capacity analysis of remote radio head associations in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "channel matrix sparsity with imperfect channel state information in cloud radio access networks")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse optimization for green edge ai inference")
  Member "on peak versus average interference power constraints for protecting primary users in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transmit power policy and ergodic multicast rate analysis of cognitive radio networks in generalized fading")

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)