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: "compressed sensing on the image of bilinear maps"
  Abstract: For several communication models, the dispersive part of a communication channel is described by a bilinear operation $T$ between the possible sets of input signals and channel parameters. The received channel output has then to be identified from the image $T(X,Y)$ of the input signal difference sets $X$ and the channel state sets $Y$. The main goal in this contribution is to characterize the compressibility of $T(X,Y)$ with respect to an ambient dimension $N$. In this paper we show that a restricted norm multiplicativity of $T$ on all canonical subspaces $X$ and $Y$ with dimension $S$ resp. $F$ is sufficient for the reconstruction of output signals with an overwhelming probability from $\mathcal{O}((S+F)\log N)$ random sub-Gaussian measurements.

Connected through hyperedge HE-1 (induced by source paper "blind deconvolution with additional autocorrelations via convex programs"):
  with members:
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "on the stability of sparse convolutions"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "stable recovery from the magnitude of symmetrized fourier measurements"

Connected through hyperedge HE-2 (induced by source paper "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"):
  with members:
    - "identifiability scaling laws in bilinear inverse problems"
    - "on the stability of sparse convolutions"
    - "ripless compressed sensing from anisotropic measurements"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "identifiability scaling laws in bilinear inverse problems"):
  with members:
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "sampling and recovery of pulse streams"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "stable recovery from the magnitude of symmetrized fourier measurements"):
  with members:
    - "phase retrieval for sparse signals"
    - "phase retrieval stability and recovery guarantees"
    - "approximation of lowdin orthogonalization to a spectrally efficient orthogonal overlapping ppm design for uwb impulse radio"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-5 (induced by source paper "a stability result for sparse convolutions"):
  with members:
    - "stable recovery from the magnitude of symmetrized fourier measurements"

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)