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: "discrete signal reconstruction by sum of absolute values"
  Abstract: In this letter, we consider a problem of reconstructing an unknown discrete signal taking values in a finite alphabet from incomplete linear measurements. The difficulty of this problem is that the computational complexity of the reconstruction is exponential as it is. To overcome this difficulty, we extend the idea of compressed sensing, and propose to solve the problem by minimizing the sum of weighted absolute values. We assume that the probability distribution defined on an alphabet is known, and formulate the reconstruction problem as linear programming. Examples are shown to illustrate that the proposed method is effective.

Connected through hyperedge HE-1 (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 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"

Connected through hyperedge HE-2 (induced by source paper "deep learning aided projected gradient detector for massive overloaded mimo channels"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "fifty years of mimo detection the road to large scale mimos"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "discrete valued control by sum of absolute values optimization"):
  with members:
    - "maximum hands off control a paradigm of control effort minimization"
    - "continuity of the value function in sparse optimal control"

Connected through hyperedge HE-4 (induced by source paper "multiuser detection by map estimation with sum of absolute values relaxation"):
  with members:
    - "reduced dimension multiuser detection"

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)