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 user association data delivery rate and precoding for cache enabled f rans"):
  Abstract: This paper considers the downlink of a cache-enabled fog radio access network (F-RAN) with limited fronthaul capacity, where user association (UA), data delivery rate (DDR) and signal precoding are jointly optimized. We formulate a mixed-integer nonlinear programming problem in which the weighted difference of network throughput and total power consumption is maximized, subject to the predefined DDR requirements and the maximum transmit power at each eRRH. To address this challenging problem, we first apply the l0-norm approximation and l1-norm minimization techniques to deal with the UA. After this key step, we arrive at an approximated problem that only involves the joint optimization of DDR and precoding. By using the alternating descent method, we further decompose this problem into a convex subproblem for DDR allocation and a nonconvex subproblem for precoding design. While the former is globally solved by the interior-point method, the latter is solved by a specifically tailored successive convex quadratic programming method. Finally, we propose an iterative algorithm for the original joint optimization that is guaranteed to converge. Importantly, each iteration of the developed algorithm only involves solving simple convex problems. Numerical examples demonstrate that the proposed design significantly improves both throughput and power performances, especially in practical F-RANs with limited fronthaul capacity. Compared to the sole precoder design for a given cache placement, our joint design is shown to improve the throughput by 50% while saving at least half of the total power consumption in the considered examples.

Includes members:
  Member "fog computing based radio access networks issues and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach")
  Member "user association in 5g networks a survey and an outlook":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tutorial on beam management for 3gpp nr at mmwave frequencies")
  Member "energy efficiency of downlink transmission strategies for cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse optimization for green edge ai inference")
  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 "large scale convex optimization for ultra dense cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")

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)