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: "new conditions for sparse phase retrieval"
  Abstract: We consider the problem of sparse phase retrieval, where a $k$-sparse signal ${\bf x} \in {\mathbb R}^n \textrm{ (or } {\mathbb C}^n\textrm{)}$ is measured as ${\bf y} = |{\bf Ax}|,$ where ${\bf A} \in {\mathbb R}^{m \times n} \textrm{ (or } {\mathbb C}^{m \times n}\textrm{ respectively)}$ is a measurement matrix and $|\cdot|$ is the element-wise absolute value. For a real signal and a real measurement matrix ${\bf A}$, we show that $m = 2k$ measurements are necessary and sufficient to recover ${\bf x}$ uniquely. For complex signal ${\bf x} \in {\mathbb C}^n$ and ${\bf A} \in {\mathbb C}^{m \times n}$, we show that $m = 4k-2$ phaseless measurements are sufficient to recover ${\bf x}$. It is known that the multiplying constant $4$ in $m = 4k-2$ cannot be improved.

Connected through hyperedge HE-1 (induced by source paper "almost optimal phaseless compressed sensing with sublinear decoding time"):
  with members:
    - "phasemax convex phase retrieval via basis pursuit"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"
    - "fast and robust compressive phase retrieval with sparse graph codes"
    - "heavy hitters via cluster preserving clustering"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "super sparse signals with unknown phases efficiently recovered"

Connected through hyperedge HE-2 (induced by source paper "on fast decoding of high dimensional signals from one bit measurements"):
  with members:
    - "phase retrieval an overview of recent developments"
    - "fast compressive phase retrieval from fourier measurements"
    - "for all sparse recovery in near optimal time"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "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-3 (induced by source paper "almost lossless analog compression without phase information"):
  with members:
    - "information theoretic limits of matrix completion"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "almost lossless analog signal separation"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-4 (induced by source paper "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "reliable deniable and hidable communication over multipath networks"
    - "super sparse signals with unknown phases efficiently recovered"
    - "phase retrieval for sparse signals"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "near optimal phase retrieval of sparse vectors"
    - "phase retrieval from low rate samples"

Connected through hyperedge HE-5 (induced by source paper "super sparse signals with unknown phases efficiently recovered"):
  with members:
    - "gespar efficient phase retrieval of sparse signals"
    - "near optimal phase retrieval of sparse vectors"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse phase retrieval convex algorithms and limitations"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"

Connected through hyperedge HE-6 (induced by source paper "fast compressive phase retrieval from fourier measurements"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "phase retrieval via structured modulations in paley wiener spaces"
    - "phaseless signal recovery in infinite dimensional spaces using structured modulations"
    - "phase retrieval from low rate samples"
    - "phase retrieval from coded diffraction patterns"
    - "sparse signal recovery from quadratic measurements via convex programming"

Connected through hyperedge HE-7 (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"
    - "fast compressive phase retrieval from fourier measurements"
    - "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"
    - "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)