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 "deep learning based precoding for the mimo gaussian wiretap channel"):
  Abstract: A novel precoding method based on supervised deep neural networks is introduced for the multiple-input multiple-output Gaussian wiretap channel. The proposed deep learning (DL)-based precoding learns the input covariance matrix through offline training over a large set of input channels and their corresponding covariance matrices for efficient, reliable, and secure transmission of information. Furthermore, by spending time in offline training, this method remarkably reduces the computation complexity in real-time applications. Compared to traditional precoding methods, the proposed DL-based precoding is significantly faster and reaches near-capacity secrecy rates. DL-based precoding is also more robust than transitional precoding approaches to the number of antennas at the eavesdropper. This new approach to precoding is promising in applications in which delay and complexity are critical.

Includes members:
  Member "a rotation based method for precoding in gaussian mimome channels":
    also connected through other hyperedges:
      (none)
  Member "interplay between noma and other emerging technologies a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rate analysis of cell free massive mimo noma with three linear precoders")
  Member "end to end learning of communications systems without a channel model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems")
  Member "deep learning for the gaussian wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conditional mutual information neural estimator")
  Member "deep learning in mobile and wireless networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "optimal beamforming for gaussian mimo wiretap channels with two transmit antennas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interplay between noma and other emerging technologies a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)