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 "jointly sparse signal recovery via deep auto encoder and parallel coordinate descent unrolling"):
  Abstract: In this paper, utilizing techniques in compressed sensing, parallel optimization and deep learning, we propose a model-driven approach to jointly design the common measurement matrix and GROUP LASSO-based jointly sparse signal recovery method for complex sparse signals, based on the standard auto-encoder structure for real numbers. The encoder achieves noisy linear compression for jointly sparse signals, with a common measurement matrix. The GROUP LASSO-based decoder realizes jointly sparse signal recovery based on an iterative parallel-coordinate descent (PCD) algorithm which is proposed to solve GROUP LASSO in a parallel manner. In particular, the decoder consists of an approximation part which unfolds (several iterations of) the proposed iterative algorithm to obtain an approximate solution of GROUP LASSO and a correction part which reduces the difference between the approximate solution and the actual jointly sparse signals. The proposed model-driven approach achieves higher recovery accuracy with less computation time than the classic GROUP LASSO method, and the gain significantly increases in the presence of extra structures in sparse patterns. The common measurement matrix obtained by the proposed model-driven approach is also suitable for the classic GROUP LASSO method. We consider an application example, i.e., channel estimation in Multiple-Input Multiple-Output (MIMO)-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). By numerical results, we demonstrate the substantial gains of the proposed model-driven approach over GROUP LASSO and AMP when the number of jointly sparse signals is not very large.

Includes members:
  Member "improved scaling law for activity detection in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "grant free massive mtc enabled massive mimo a compressive sensing approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "sparse activity detection for massive connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse optimization for green edge ai inference")
  Member "massive connectivity with massive mimo part i device activity detection and channel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "deep learning sparse ternary projections for compressed sensing of images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep coupled representation learning for sparse linear inverse problems with side information")
  Member "deep networks for compressed image sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "deep learning for massive mimo csi feedback":
    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)