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 "enabling fdd massive mimo through deep learning based channel prediction"):
  Abstract: A major obstacle for widespread deployment of frequency division duplex (FDD)-based Massive multiple-input multiple-output (MIMO) communications is the large signaling overhead for reporting full downlink (DL) channel state information (CSI) back to the basestation (BS), in order to enable closed-loop precoding. We completely remove this overhead by a deep-learning based channel extrapolation (or "prediction") approach and demonstrate that a neural network (NN) at the BS can infer the DL CSI centered around a frequency $f_\text{DL}$ by solely observing uplink (UL) CSI on a different, yet adjacent frequency band around $f_\text{UL}$; no more pilot/reporting overhead is needed than with a genuine time division duplex (TDD)-based system. The rationale is that scatterers and the large-scale propagation environment are sufficiently similar to allow a NN to learn about the physical connections and constraints between two neighboring frequency bands, and thus provide a well-operating system even when classic extrapolation methods, like the Wiener filter (used as a baseline for comparison throughout) fails. We study its performance for various state-of-the-art Massive MIMO channel models, and, even more so, evaluate the scheme using actual Massive MIMO channel measurements, rendering it to be practically feasible at negligible loss in spectral efficiency when compared to a genuine TDD-based system.

Includes members:
  Member "massive mimo has unlimited capacity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "compressive sensing techniques for next generation wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "power of deep learning for channel estimation and signal detection in ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "on deep learning based channel decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "design of capacity approaching low density parity check codes using recurrent neural networks")
  Member "massive mimo ten myths and one critical question":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "achievable rates of fdd massive mimo systems with spatial channel correlation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")

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)