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 "on the precoder design of a wireless energy harvesting node in linear vector gaussian channels with arbitrary input distribution"):
  Abstract: A Wireless Energy Harvesting Node (WEHN) operating in linear vector Gaussian channels with arbitrarily distributed input symbols is considered in this paper. The precoding strategy that maximizes the mutual information along N independent channel accesses is studied under non-causal knowledge of the channel state and harvested energy (commonly known as offline approach). It is shown that, at each channel use, the left singular vectors of the precoder are equal to the eigenvectors of the Gram channel matrix. Additionally, an expression that relates the optimal singular values of the precoder with the energy harvesting profile through the Minimum Mean-Square Error (MMSE) matrix is obtained. Then, the specific situation in which the right singular vectors of the precoder are set to the identity matrix is considered. In this scenario, the optimal offline power allocation, named Mercury Water-Flowing, is derived and an intuitive graphical representation is presented. Two optimal offline algorithms to compute the Mercury Water- Flowing solution are proposed and an exhaustive study of their computational complexity is performed. Moreover, an online algorithm is designed, which only uses causal knowledge of the harvested energy and channel state. Finally, the achieved mutual information is evaluated through simulation.

Includes members:
  Member "optimal energy allocation for wireless communications with energy harvesting constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems")
  Member "transmission with energy harvesting nodes in fading wireless channels optimal policies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "closed form analysis of non linear age of information in status updates with an energy harvesting transmitter")
  Member "estimation in gaussian noise properties of the minimum mean square error":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed coding amp based decoding and analog spatial coupling")
  Member "optimum transmission policies for battery limited energy harvesting nodes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")
  Member "optimal packet scheduling in an energy harvesting communication system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely updates in energy harvesting two hop networks offline and online policies")
  Member "hessian and concavity of mutual information differential entropy and entropy power in linear vector gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the geometry of community detection via the mmse matrix")
  Member "on optimal precoding in linear vector gaussian channels with arbitrary input distribution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "measurement matrix design for phase retrieval based on mutual information")
  Member "mutual information and minimum mean square error in gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)