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 "large scale convex optimization for ultra dense cloud ran"):
  Abstract: The heterogeneous cloud radio access network (Cloud-RAN) provides a revolutionary way to densify radio access networks. It enables centralized coordination and signal processing for efficient interference management and flexible network adaptation. Thus, it can resolve the main challenges for next-generation wireless networks, including higher energy efficiency and spectral efficiency, higher cost efficiency, scalable connectivity, and low latency. In this article, we shall provide an algorithmic thinking on the new design challenges for the dense heterogeneous Cloud-RAN based on convex optimization. As problem sizes scale up with the network size, we will demonstrate that it is critical to take unique structures of design problems and inherent characteristics of wireless channels into consideration, while convex optimization will serve as a powerful tool for such purposes. Network power minimization and channel state information acquisition will be used as two typical examples to demonstrate the effectiveness of convex optimization methods. We will then present a two-stage framework to solve general large-scale convex optimization problems, which is amenable to parallel implementation in the cloud data center.

Includes members:
  Member "large scale convex optimization for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale beamforming for massive mimo via randomized sketching")
  Member "heterogeneous cloud radio access networks a new perspective for enhancing spectral and energy efficiencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "scalable coordinated beamforming for dense wireless cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low complexity antenna selection for low target rate users in dense cloud radio access networks")
  Member "base station activation and linear transceiver design for optimal resource management in heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "millimeter wave cellular wireless networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "sparse beamforming and user centric clustering for downlink cloud radio access network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse optimization for green edge ai inference")
  Member "optimal stochastic coordinated beamforming for wireless cooperative networks with csi uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "energy efficient processing and robust wireless cooperative transmission for edge inference")

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)