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: "complex field trainable ista for linear and nonlinear inverse problems"
  Abstract: Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications. In this paper, we propose a trainable iterative signal recovery algorithm named complex-field TISTA (C-TISTA) which treats complex-field nonlinear inverse problems. C-TISTA is based on the concept of deep unfolding and consists of a gradient descent step with the Wirtinger derivatives followed by a shrinkage step with a trainable complex-valued shrinkage function. Importantly, it contains a small number of trainable parameters so that its training process can be executed efficiently. Numerical results indicate that C-TISTA shows remarkable signal recovery performance compared with existing algorithms.

Connected through hyperedge HE-1 (induced by source paper "chebyshev inertial landweber algorithm for linear inverse problems"):
  with members:
    - "theoretical interpretation of learned step size in deep unfolded gradient descent"

Connected through hyperedge HE-2 (induced by source paper "trainable projected gradient detector for sparsely spread code division multiple access"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "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-3 (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"
    - "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"

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)