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 embeddings with structured hashed projections"
  Abstract: We consider the hashing mechanism for constructing binary embeddings, that involves pseudo-random projections followed by nonlinear (sign function) mappings. The pseudo-random projection is described by a matrix, where not all entries are independent random variables but instead a fixed "budget of randomness" is distributed across the matrix. Such matrices can be efficiently stored in sub-quadratic or even linear space, provide reduction in randomness usage (i.e. number of required random values), and very often lead to computational speed ups. We prove several theoretical results showing that projections via various structured matrices followed by nonlinear mappings accurately preserve the angular distance between input high-dimensional vectors. To the best of our knowledge, these results are the first that give theoretical ground for the use of general structured matrices in the nonlinear setting. In particular, they generalize previous extensions of the Johnson-Lindenstrauss lemma and prove the plausibility of the approach that was so far only heuristically confirmed for some special structured matrices. Consequently, we show that many structured matrices can be used as an efficient information compression mechanism. Our findings build a better understanding of certain deep architectures, which contain randomly weighted and untrained layers, and yet achieve high performance on different learning tasks. We empirically verify our theoretical findings and show the dependence of learning via structured hashed projections on the performance of neural network as well as nearest neighbor classifier.

Connected through hyperedge HE-1 (induced by source paper "provable filter pruning for efficient neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "learning filter basis for convolutional neural network compression"
    - "the state of sparsity in deep neural networks"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-2 (induced by source paper "sipping neural networks sensitivity informed provable pruning of neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "on coresets for logistic regression"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "data dependent coresets for compressing neural networks with applications to generalization bounds"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "training support vector machines using coresets"

Connected through hyperedge HE-3 (induced by source paper "data dependent coresets for compressing neural networks with applications to generalization bounds"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "compression aware training of deep networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "simple classification using binary data"):
  with members:
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-6 (induced by source paper "structured adaptive and random spinners for fast machine learning computations"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "practical and optimal lsh for angular distance"
    - "randomized sketches of convex programs with sharp guarantees"
    - "scalable kernel methods via doubly stochastic gradients"
    - "convolutional kernel networks"
    - "predicting parameters in deep learning"
    - "a sparse johnson lindenstrauss transform"

Connected through hyperedge HE-7 (induced by source paper "recycling randomness with structure for sublinear time kernel expansions"):
  with members:
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "fastfood approximate kernel expansions in loglinear time"
    - "randomized algorithms for matrices and data"
    - "restricted isometries for partial random circulant matrices"

Connected through hyperedge HE-8 (induced by source paper "triplespin a generic compact paradigm for fast machine learning computations"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "practical and optimal lsh for angular distance"
    - "randomized sketches of convex programs with sharp guarantees"

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.LG(Machine Learning)