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 "artificial noise aided biobjective transmitter optimization for service integration in multi user mimo broadcast channel"):
  Abstract: This paper considers an artificial noise (AN)-aided transmit design for multi-user MIMO systems with integrated services. Specifically, two sorts of service messages are combined and served simultaneously: one multicast message intended for all receivers and one confidential message intended for only one receiver and required to be perfectly secure from other unauthorized receivers. Our interest lies in the joint design of input covariances of the multicast message, confidential message, and artificial noise (AN), such that the achievable secrecy rate and multicast rate are simultaneously maximized. This problem is identified as a secrecy rate region maximization (SRRM) problem in the context of physical-layer service integration. Since this biobjective optimization problem is inherently complex to solve, we put forward two different scalarization methods to convert it into a scalar optimization problem. First, we propose to prefix the multicast rate as a constant, and accordingly, the primal biobjective problem is converted into a secrecy rate maximization (SRM) problem with quality of multicast service (QoMS) constraint. By varying the constant, we can obtain different Pareto optimal points. The resulting SRM problem can be iteratively solved via a provably convergent difference-of-concave (DC) algorithm. In the second method, we aim to maximize the weighted sum of the secrecy rate and the multicast rate. Through varying the weighted vector, one can also obtain different Pareto optimal points. We show that this weighted sum rate maximization (WSRM) problem can be recast into a primal decomposable form, which is amenable to alternating optimization (AO). Then, we compare these two scalarization methods in terms of their overall performance and computational complexity via theoretical analysis as well as numerical simulation, based on which new insights can be drawn.

Includes members:
  Member "gsvd based precoding in mimo systems with integrated services":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noma an information theoretic perspective")
  Member "robust artificial noise aided transmit design for multi user miso systems with integrated services":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physical layer service integration in 5g potentials and challenges")
  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 "principles of physical layer security in multiuser wireless networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "angle aware user cooperation for secure massive mimo in rician fading channel")
  Member "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust transmissions in wireless powered multi relay networks with chance interference constraints")
  Member "spatially selective artificial noise aided transmit optimization for miso multi eves secrecy rate maximization":
    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 "wireless physical layer security with imperfect channel state information a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blind channel separation in massive mimo system under pilot spoofing and jamming attack")
  Member "new results on multiple input multiple output broadcast channels with confidential messages":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vector gaussian successive refinement with degraded side information")

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)