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: "the high dimensional geometry of binary neural networks"
  Abstract: Recent research has shown that one can train a neural network with binary weights and activations at train time by augmenting the weights with a high-precision continuous latent variable that accumulates small changes from stochastic gradient descent. However, there is a dearth of theoretical analysis to explain why we can effectively capture the features in our data with binary weights and activations. Our main result is that the neural networks with binary weights and activations trained using the method of Courbariaux, Hubara et al. (2016) work because of the high-dimensional geometry of binary vectors. In particular, the ideal continuous vectors that extract out features in the intermediate representations of these BNNs are well-approximated by binary vectors in the sense that dot products are approximately preserved. Compared to previous research that demonstrated the viability of such BNNs, our work explains why these BNNs work in terms of the HD geometry. Our theory serves as a foundation for understanding not only BNNs but a variety of methods that seek to compress traditional neural networks. Furthermore, a better understanding of multilayer binary neural networks serves as a starting point for generalizing BNNs to other neural network architectures such as recurrent neural networks.

Connected through hyperedge HE-1 (induced by source paper "sparse weight activation training"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "swalp stochastic weight averaging in low precision training"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "per tensor fixed point quantization of the back propagation algorithm"

Connected through hyperedge HE-2 (induced by source paper "least squares binary quantization of neural networks"):
  with members:
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "automatic pruning for quantized neural networks"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "discrimination aware channel pruning for deep neural networks"
    - "relaxed quantization for discretized neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "latent weights do not exist rethinking binarized neural network optimization"):
  with members:
    - "back to simplicity how to train accurate bnns from scratch"
    - "improved training of binary networks for human pose estimation and image recognition"
    - "regularizing activation distribution for training binarized deep networks"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "model compression via distillation and quantization"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "structured binary neural networks for accurate image classification and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "a tandem learning rule for efficient and rapid inference on deep spiking neural networks"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "direct training for spiking neural networks faster larger better"
    - "slayer spike layer error reassignment in time"

Connected through hyperedge HE-6 (induced by source paper "fq conv fully quantized convolution for efficient and accurate inference"):
  with members:
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "deep neural network compression with single and multiple level quantization"
    - "label refinery improving imagenet classification through label progression"
    - "deep residual learning for small footprint keyword spotting"
    - "hello edge keyword spotting on microcontrollers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "hadanets flexible quantization strategies for neural networks"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "universal deep neural network compression"
    - "compressing neural networks using the variational information bottleneck"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-8 (induced by source paper "density encoding enables resource efficient randomly connected neural networks"):
  with members:
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "bitwise neural networks"
    - "ternary neural networks for resource efficient ai applications"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "representing objects relations and sequences"
    - "provably bounded optimal agents"

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)