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: "deep learning aided trainable projected gradient decoding for ldpc codes"
  Abstract: We present a novel optimization-based decoding algorithm for LDPC codes that is suitable for hardware architectures specialized to feed-forward neural networks. The algorithm is based on the projected gradient descent algorithm with a penalty function for solving a non-convex minimization problem. The proposed algorithm has several internal parameters such as step size parameters, a softness parameter, and the penalty coefficients. We use a standard tool set of deep learning, i.e., back propagation and stochastic gradient descent (SGD) type algorithms, to optimize these parameters. Several numerical experiments show that the proposed algorithm outperforms the belief propagation decoding in some cases.

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"
    - "trainable ista for sparse signal recovery"
    - "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 "complex field trainable ista for linear and nonlinear inverse problems"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "model driven deep learning for joint mimo channel estimation and signal detection"
    - "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-3 (induced by source paper "trainable projected gradient detector for sparsely spread code division multiple access"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "complex field trainable ista for linear and nonlinear inverse problems"
    - "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-4 (induced by source paper "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "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 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"

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)