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 "energy harvesting in secure mimo systems"):
  Abstract: The problems of energy harvesting in wireless com- munication systems have recently drawn much attention. In this paper, we focus on the investigation of energy harvesting maximization (EHM) in the important secrecy multi-input multi- output (MIMO) systems where little research has been done due to their complexity. Particularly, this paper studies the resource allocation strategies in MIMO wiretap channels, wherein we attempt to maximize the harvested energy by one or multiple multi-antenna energy receivers (ERs) (potential eavesdropper) while guaranteeing the secure communication for the multi- antenna information receiver (IR). Two types of IR, with and without the capability to cancel the interference from energy signals, are taken into account. In the scenario of single energy receiver (ER), we consider the joint design of the transmis- sion information and energy covariances for EHM. Both of the optimization problems for the two types of IR are non- convex, and appear to be difficult. To circumvent them, the combination of first order Taylor approximation and sequential convex optimization approach is proposed. Then, we extend our attention to the scenario with multiple ERs, where the artificial noise (AN) aided weighted sum-energy harvesting maximization (WS-EHM) problem is considered. Other than the approaches adopted in solving the EHM problems, an algorithm conducts in an alternating fashion is proposed to handle this problem. In particular, we first perform a judicious transformation of the WS- EHM problem. Then, a block Gauss-Seidel (GS) algorithm based on logarithmic barrier method and gradient projection (GP) is derived to obtain the optimal solution of the reformulation by solving convex problems alternately. Furthermore, the resulting block GS method is proven to converge to a Karush-Kuhn-Tucker (KKT) point of the original WS-EHM problem...

Includes members:
  Member "an algorithm for global maximization of secrecy rates in gaussian mimo wiretap channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure mimo transmission via intelligent reflecting surface")
  Member "energy harvesting for physical layer security in ofdma networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secrecy outage of swipt in the presence of cooperating eavesdroppers")
  Member "robust beamforming for secure communication in systems with wireless information and power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "angle aware user cooperation for secure massive mimo in rician fading channel")
  Member "secure beamforming for mimo broadcasting with wireless information and power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust beamforming and jamming for enhancing the physical layer security of full duplex radios")
  Member "max min fair wireless energy transfer for secure multiuser communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "max min fair beamforming for swipt systems with non linear eh model")
  Member "secrecy wireless information and power transfer with miso beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient resource allocation for secure noma enabled mobile edge computing networks")
  Member "multiuser miso beamforming for simultaneous wireless information and power transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "design of an miso swipt aided code index modulated multi carrier m dcsk system for e health iot")
  Member "spatially selective artificial noise aided transmit optimization for miso multi eves secrecy rate maximization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")

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)