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: "low latency privacy preserving inference"
  Abstract: When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphic Encryption with neural networks to make inferences while protecting against information leakage. However, these methods are limited by the width and depth of neural networks that can be used (and hence the accuracy) and exhibit high latency even for relatively simple networks. In this study we provide two solutions that address these limitations. In the first solution, we present more than $10\times$ improvement in latency and enable inference on wider networks compared to prior attempts with the same level of security. The improved performance is achieved by novel methods to represent the data during the computation. In the second solution, we apply the method of transfer learning to provide private inference services using deep networks with latency of $\sim0.16$ seconds. We demonstrate the efficacy of our methods on several computer vision tasks.

Connected through hyperedge HE-1 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition"):
  with members:
    - "privacy preserving image based localization"
    - "xonn xnor based oblivious deep neural network inference"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "gazelle a low latency framework for secure neural network inference"
    - "stealing hyperparameters in machine learning"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "deepsecure scalable provably secure deep learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-2 (induced by source paper "she a fast and accurate privacy preserving deep neural network via leveled tfhe and logarithmic data representation"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deepsecure scalable provably secure deep learning"
    - "shiftcnn generalized low precision architecture for inference of convolutional neural networks"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "ngraph he2 a high throughput framework for neural network inference on encrypted data"):
  with members:
    - "toward scalable fully homomorphic encryption through light trusted computing assistance"
    - "xonn xnor based oblivious deep neural network inference"
    - "sealion a framework for neural network inference on encrypted data"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"

Connected through hyperedge HE-4 (induced by source paper "eva an encrypted vector arithmetic language and compiler for efficient homomorphic computation"):
  with members:
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "gazelle a low latency framework for secure neural network inference"
    - "cryptodl deep neural networks over encrypted data"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deepsecure scalable provably secure deep learning"
    - "optimizing relinearization in circuits for homomorphic encryption"

Connected through hyperedge HE-5 (induced by source paper "she a fast and accurate deep neural network for encrypted data"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "shiftcnn generalized low precision architecture for inference of convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-6 (induced by source paper "glyph fast and accurately training deep neural networks on encrypted data"):
  with members:
    - "quotient two party secure neural network training and prediction"
    - "swalp stochastic weight averaging in low precision training"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "how transferable are features in deep neural networks"
    - "convolutional neural networks applied to house numbers digit classification"

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)