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 "beyond 5g leveraging cell free tdd massive mimo using cascaded deep learning"):
  Abstract: Cell Free Massive MIMO is a solution for improving the spectral efficiency of next generation communication systems and a crucial aspect for realizing the gains of the technology is the availability of accurate Channel State Information (CSI). Time Division Duplexing (TDD) mode is popular for Cell Free Massive MIMO since the physical wireless channel's assumed reciprocity facilitates channel estimation. However, the availability of accurate CSI in the TDD mode is hindered by the non reciprocity of the end to end channel, due to the presence of RF components, as well as the non availability of CSI in the subcarriers that do not have reference signals. Hence, the prediction of the Downlink CSI in the subcarriers without reference signals becomes an even more complicated problem. In this work, we consider TDD non-reciprocity with limited availability of resource elements for CSI estimation and propose a deep learning based approach using cascaded Deep Neural Networks (DNNs) to attain a one shot prediction of the reverse channel across the entire bandwidth. The proposed method is able to estimate downlink CSI at all subcarriers from the uplink CSI at selected subcarriers and hence does not require downlink CSI feedback.

Includes members:
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "a framework for over the air reciprocity calibration for tdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "amplitude and phase estimation for absolute calibration of massive mimo front ends")
  Member "massive mimo performance with imperfect channel reciprocity and channel estimation error":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "achieving phase coherency and gain stability in active antenna arrays for sub 6 ghz fdd and tdd fd mimo challenges and solutions")
  Member "power of deep learning for channel estimation and signal detection in ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "cell free massive mimo versus small cells":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "reciprocity calibration for massive mimo proposal modeling and validation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "amplitude and phase estimation for absolute calibration of massive mimo front ends")

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)