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: "near optimal sample complexity bounds for circulant binary embedding"
  Abstract: Binary embedding is the problem of mapping points from a high-dimensional space to a Hamming cube in lower dimension while preserving pairwise distances. An efficient way to accomplish this is to make use of fast embedding techniques involving Fourier transform e.g.~circulant matrices. While binary embedding has been studied extensively, theoretical results on fast binary embedding are rather limited. In this work, we build upon the recent literature to obtain significantly better dependencies on the problem parameters. A set of $N$ points in $\mathbb{R}^n$ can be properly embedded into the Hamming cube $\{\pm 1\}^k$ with $\delta$ distortion, by using $k\sim\delta^{-3}\log N$ samples which is optimal in the number of points $N$ and compares well with the optimal distortion dependency $\delta^{-2}$. Our optimal embedding result applies in the regime $\log N\lesssim n^{1/3}$. Furthermore, if the looser condition $\log N\lesssim \sqrt{n}$ holds, we show that all but an arbitrarily small fraction of the points can be optimally embedded. We believe our techniques can be useful to obtain improved guarantees for other nonlinear embedding problems.

Connected through hyperedge HE-1 (induced by source paper "recent advances in convolutional neural network acceleration"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "mec memory efficient convolution for deep neural network"
    - "coordinating filters for faster deep neural networks"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "channel pruning for accelerating very deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"

Connected through hyperedge HE-3 (induced by source paper "reliable identification of redundant kernels for convolutional neural network compression"):
  with members:
    - "learning efficient convolutional networks through network slimming"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "convolutional neural networks with low rank regularization"
    - "you only look once unified real time object detection"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-4 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"

Connected through hyperedge HE-5 (induced by source paper "fast binary embeddings with gaussian circulant matrices improved bounds"):
  with members:
    - "binary embedding fundamental limits and fast algorithm"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "on binary embedding using circulant matrices"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-6 (induced by source paper "small width low distortions quantized random embeddings of low complexity sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

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)