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: "sample optimal average case sparse fourier transform in two dimensions"
  Abstract: We present the first sample-optimal sublinear time algorithms for the sparse Discrete Fourier Transform over a two-dimensional sqrt{n} x sqrt{n} grid. Our algorithms are analyzed for /average case/ signals. For signals whose spectrum is exactly sparse, our algorithms use O(k) samples and run in O(k log k) time, where k is the expected sparsity of the signal. For signals whose spectrum is approximately sparse, our algorithm uses O(k log n) samples and runs in O(k log^2 n) time; the latter algorithm works for k=Theta(sqrt{n}). The number of samples used by our algorithms matches the known lower bounds for the respective signal models. #R##N#By a known reduction, our algorithms give similar results for the one-dimensional sparse Discrete Fourier Transform when n is a power of a small composite number (e.g., n = 6^t).

Connected through hyperedge HE-1 (induced by source paper "a fast face detection method via convolutional neural network"):
  with members:
    - "object discovery via cohesion measurement"
    - "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"
    - "scale aware face detection"
    - "face detection using deep learning an improved faster rcnn approach"
    - "face detection with the faster r cnn"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "you only look once unified real time object detection"
    - "learning multi domain convolutional neural networks for visual tracking"

Connected through hyperedge HE-2 (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-3 (induced by source paper "an adaptive sublinear time block sparse fourier transform"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "group sparse model selection hardness and relaxations"
    - "on model based rip 1 matrices"
    - "nearly optimal sparse fourier transform"
    - "1 eps approximate sparse recovery"
    - "lower bounds for sparse recovery"

Connected through hyperedge HE-4 (induced by source paper "sample efficient estimation and recovery in sparse fft via isolation on average"):
  with members:
    - "an adaptive sublinear time block sparse fourier transform"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "lower bounds for sparse recovery"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-5 (induced by source paper "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "the restricted isometry property of subsampled fourier matrices"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "nearly optimal sparse fourier transform"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "lower bounds for sparse recovery"
    - "a probabilistic and ripless theory of compressed sensing"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-6 (induced by source paper "a sparse multidimensional fft for real positive vectors"):
  with members:
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "fast and efficient sparse 2d discrete fourier transform using sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"

Connected through hyperedge HE-7 (induced by source paper "an efficient high dimensional sparse fourier transform"):
  with members:
    - "fast and efficient sparse 2d discrete fourier transform using sparse graph codes"
    - "nearly optimal sparse fourier transform"

Connected through hyperedge HE-8 (induced by source paper "spright a fast and robust framework for sparse walsh hadamard transform"):
  with members:
    - "nearly optimal deterministic algorithm for sparse walsh hadamard transform"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "a fast hadamard transform for signals with sub linear sparsity in the transform domain"
    - "polarization of the r enyi information dimension for single and multi terminal analog compression"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "nearly optimal sparse fourier transform"

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.DS(Data Structures and Algorithms)