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 "model driven deep learning for joint mimo channel estimation and signal detection"):
  Abstract: In this paper, we investigate the model-driven deep learning (DL) for joint MIMO channel estimation and signal detection (JCESD), where signal detection considers channel estimation error and channel statistics while channel estimation is refined by detected data and takes the signal detection error into consideration. In particular, the MIMO signal detector is specially designed by unfolding an iterative algorithm and adding some trainable parameters. Since the number of trainable parameters is much fewer than the data-driven DL based signal detector, the model-driven DL based MIMO signal detector can be rapidly trained with a much smaller data set. Furthermore, the proposed signal detector can be extended to soft-input soft-output detection easily. Based on numerical results, the model-driven DL based JCESD scheme significantly improves the performance of the corresponding traditional iterative detector and the signal detector exhibits superior robustness to signal-to-noise ratio (SNR) and channel correlation mismatches.

Includes members:
  Member "trainable ista for sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "deep learning based channel estimation for beamspace mmwave massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep learning in physical layer communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "theoretical linear convergence of unfolded ista and its practical weights and thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "model driven deep learning for physical layer communications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "a model driven deep learning network for mimo detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")

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)