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: "binary embedding fundamental limits and fast algorithm"
  Abstract: Binary embedding is a nonlinear dimension reduction methodology where high dimensional data are embedded into the Hamming cube while preserving the structure of the original space. Specifically, for an arbitrary $N$ distinct points in $\mathbb{S}^{p-1}$, our goal is to encode each point using $m$-dimensional binary strings such that we can reconstruct their geodesic distance up to $\delta$ uniform distortion. Existing binary embedding algorithms either lack theoretical guarantees or suffer from running time $O\big(mp\big)$. We make three contributions: (1) we establish a lower bound that shows any binary embedding oblivious to the set of points requires $m = \Omega(\frac{1}{\delta^2}\log{N})$ bits and a similar lower bound for non-oblivious embeddings into Hamming distance; (2) we propose a novel fast binary embedding algorithm with provably optimal bit complexity $m = O\big(\frac{1}{\delta^2}\log{N}\big)$ and near linear running time $O(p \log p)$ whenever $\log N \ll \delta \sqrt{p}$, with a slightly worse running time for larger $\log N$; (3) we also provide an analytic result about embedding a general set of points $K \subseteq \mathbb{S}^{p-1}$ with even infinite size. Our theoretical findings are supported through experiments on both synthetic and real data sets.

Connected through hyperedge HE-1 (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"
    - "near optimal sample complexity bounds for circulant binary embedding"

Connected through hyperedge HE-2 (induced by source paper "compressing deep neural networks a new hashing pipeline using kac s random walk matrices"):
  with members:
    - "compressing neural networks with the hashing trick"
    - "adam a method for stochastic optimization"
    - "experiments with random projection"

Connected through hyperedge HE-3 (induced by source paper "fast binary embeddings with gaussian circulant matrices improved bounds"):
  with members:
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "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-4 (induced by source paper "near optimal sample complexity bounds for circulant binary embedding"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "isometric sketching of any set via the restricted isometry property"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "on binary embedding using circulant matrices"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "representation and coding of signal geometry"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-6 (induced by source paper "binary embeddings with structured hashed projections"):
  with members:
    - "triplespin a generic compact paradigm for fast machine learning computations"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "compressing neural networks with the hashing trick"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "on binary embedding using circulant matrices"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "the loss surfaces of multilayer networks"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-7 (induced by source paper "near optimal bounds for binary embeddings of arbitrary sets"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "sharp time data tradeoffs for linear inverse problems"
    - "sketching as a tool for numerical linear algebra"
    - "hashing for similarity search a survey"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-8 (induced by source paper "on binary embedding using circulant matrices"):
  with members:
    - "binary embeddings with structured hashed projections"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "deep fried convnets"
    - "fastfood approximate kernel expansions in loglinear time"
    - "new bounds for circulant johnson lindenstrauss embeddings"

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)