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 efficient multi quality multicast beamforming based on scalable video coding and superposition coding"):
  Abstract: In this paper, we consider multi-quality multicast of a video stream from a multi-antenna base station (BS) to multiple groups of single-antenna users requiring different qualities of the video stream, using scalable video coding (SVC). Leveraging the layered structure of SVC and exploiting superposition coding (SC) as well as successive interference cancelation (SIC), we propose a power-efficient layer-based multi-quality multicast beamforming scheme and a power-efficient quality-based multiquality multicast beamforming scheme. For each scheme, we formulate the corresponding optimal beamforming design as a non-convex power minimization problem, and obtain a globally optimal solution in a special case as well as a locally optimal solution in the general case. Then, we show that the minimum total transmission power of the quality-based optimization problem is the same as that of the layer-based optimization problem, but the computational complexity for solving the quality-based optimization problem is lower than that for solving the layerbased optimization problem. Finally, numerical results show that the proposed solutions achieve better performance than existing solutions.

Includes members:
  Member "robust layered transmission in secure miso multiuser unicast cognitive radio systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure swipt networks based on a non linear energy harvesting model")
  Member "multi quality multicast beamforming based on scalable video coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "massive mimo multicast beamforming via accelerated random coordinate descent")

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)