human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "trainable ista for sparse signal recovery"
  Abstract: In this paper, we propose a novel sparse signal recovery algorithm called the trainable iterative soft thresholding algorithm (TISTA). The proposed algorithm consists of two estimation units: a linear estimation unit and a minimum mean squared error (MMSE) estimator based shrinkage unit. The error variance required in the MMSE shrinkage unit is precisely estimated from a tentative estimate of the original signal. The remarkable feature of the proposed scheme is that TISTA includes adjustable variables that control step size and the error variance for the MMSE shrinkage function. The variables are adjusted by standard deep learning techniques. The number of trainable variables of TISTA is nearly equal to the number of iteration rounds and is much smaller than that of known learnable sparse signal recovery algorithms. This feature leads to highly stable and fast training processes of TISTA. Computer experiments show that TISTA is applicable to various classes of sensing matrices, such as Gaussian matrices, binary matrices, and matrices with large condition numbers. Numerical results also demonstrate that, in many cases, TISTA provides significantly faster convergence than approximate message passing (AMP) and the learned iterative shrinkage thresholding algorithm and also outperforms orthogonal AMP in the NMSE performance.

Connected through hyperedge HE-1 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent"):
  with members:
    - "chebyshev inertial landweber algorithm for linear inverse problems"
    - "degrees of freedom analysis of unrolled neural networks"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "neural proximal gradient descent for compressive imaging"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"
    - "amp inspired deep networks for sparse linear inverse problems"

Connected through hyperedge HE-2 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "model driven deep learning for physical layer communications"
    - "a model driven deep learning network for mimo detection"
    - "amp inspired deep networks for sparse linear inverse problems"

Connected through hyperedge HE-3 (induced by source paper "complex field trainable ista for linear and nonlinear inverse problems"):
  with members:
    - "model driven deep learning for joint mimo channel estimation and signal detection"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "learning cluster structured sparsity by reweighting"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "adam a method for stochastic optimization"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "trainable projected gradient detector for sparsely spread code division multiple access"):
  with members:
    - "complex field trainable ista for linear and nonlinear inverse problems"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"
    - "an introduction to deep learning for the physical layer"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "deep learning aided trainable projected gradient decoding for ldpc codes"):
  with members:
    - "learning to decode linear codes using deep learning"
    - "the admm penalized decoder for ldpc codes"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"):
  with members:
    - "learning to detect"
    - "complex field trainable ista for linear and nonlinear inverse problems"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "deep learning aided projected gradient detector for massive overloaded mimo channels"
    - "a model driven deep learning network for mimo detection"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep learning based mimo communications"

Connected through hyperedge HE-8 (induced by source paper "a model driven deep learning network for mimo detection"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "admm net a deep learning approach for compressive sensing mri"
    - "deep learning for massive mimo csi feedback"
    - "message passing algorithms for compressed sensing"

Question: Please tell me which class the center node 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)