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: "fast compressive phase retrieval from fourier measurements"
  Abstract: This paper considers the problem of recovering a $k$-sparse, $N$-dimensional complex signal from Fourier magnitude measurements. It proposes a Fourier optics setup such that signal recovery up to a global phase factor is possible with very high probability whenever $M \gtrsim 4k\log_2(N/k)$ random Fourier intensity measurements are available. The proposed algorithm is comprised of two stages: An algebraic phase retrieval stage and a compressive sensing step subsequent to it. Simulation results are provided to demonstrate the applicability of the algorithm for noiseless and noisy scenarios.

Connected through hyperedge HE-1 (induced by source paper "on fast decoding of high dimensional signals from one bit measurements"):
  with members:
    - "phase retrieval an overview of recent developments"
    - "for all sparse recovery in near optimal time"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "new conditions for sparse phase retrieval"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"

Connected through hyperedge HE-2 (induced by source paper "capacity approaching phasecode for low complexity compressive phase retrieval"):
  with members:
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "sub linear time support recovery for compressed sensing using sparse graph codes"
    - "reliable deniable and hidable communication over multipath networks"
    - "super sparse signals with unknown phases efficiently recovered"
    - "new conditions for sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"

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)