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 alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
  Abstract: Fully homomorphic encryption, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned cloud applications including deep learning as a service. This comes at a high cost since FHE includes highly-intensive computation that requires enormous computing power. Although the literature includes a number of proposals to run CNNs on encrypted data, the performance is still far from satisfactory. In this paper, we push the level up and show how to accelerate the performance of running CNNs on encrypted data using GPUs. We evaluated a CNN to classify homomorphically the MNIST dataset into 10 classes. We used a number of techniques such as low-precision training, unified training and testing network, optimized FHE parameters and a very efficient GPU implementation to achieve high performance. Our solution achieved high security level (> 128 bit) and high accuracy (99%). In terms of performance, our best results show that we could classify the entire testing dataset in 14.105 seconds, with per-image amortized time (1.411 milliseconds) 40.41x faster than prior art.

Connected through hyperedge HE-1 (induced by source paper "heaan demystified accelerating fully homomorphic encryption through architecture centric analysis and optimization"):
  with members:
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "heax high performance architecture for computation on homomorphically encrypted data in the cloud"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "faster arithmetic for number theoretic transforms"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving inference in machine learning services using trusted execution environments"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "partially encrypted machine learning using functional encryption"
    - "sgxpectre stealing intel secrets from sgx enclaves via speculative execution"
    - "x gans image reconstruction made easy for extreme cases"
    - "spectre attacks exploiting speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "securing input data of deep learning inference systems via partitioned enclave execution"

Connected through hyperedge HE-3 (induced by source paper "privft private and fast text classification with homomorphic encryption"):
  with members:
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (induced by source paper "carenets compact and resource efficient cnn for homomorphic inference on encrypted medical images"):
  with members:
    - "a generic framework for privacy preserving deep learning"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "cryptodl deep neural networks over encrypted data"
    - "communication efficient learning of deep networks from decentralized data"
    - "privlogit efficient privacy preserving logistic regression by tailoring numerical optimizers"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving neural network inference on encrypted data with gpus"):
  with members:
    - "gazelle a low latency framework for secure neural network inference"
    - "cryptodl deep neural networks over encrypted data"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"

Connected through hyperedge HE-6 (induced by source paper "partially encrypted machine learning using functional encryption"):
  with members:
    - "a generic framework for privacy preserving deep learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-7 (induced by source paper "low latency privacy preserving inference"):
  with members:
    - "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "spectre returns speculation attacks using the return stack buffer"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "deep residual learning for image recognition"

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.CR(Cryptography and Security)