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: "cryptodl deep neural networks over encrypted data"
  Abstract: Machine learning algorithms based on deep neural networks have achieved remarkable results and are being extensively used in different domains. However, the machine learning algorithms requires access to raw data which is often privacy sensitive. To address this issue, we develop new techniques to provide solutions for running deep neural networks over encrypted data. In this paper, we develop new techniques to adopt deep neural networks within the practical limitation of current homomorphic encryption schemes. More specifically, we focus on classification of the well-known convolutional neural networks (CNN). First, we design methods for approximation of the activation functions commonly used in CNNs (i.e. ReLU, Sigmoid, and Tanh) with low degree polynomials which is essential for efficient homomorphic encryption schemes. Then, we train convolutional neural networks with the approximation polynomials instead of original activation functions and analyze the performance of the models. Finally, we implement convolutional neural networks over encrypted data and measure performance of the models. Our experimental results validate the soundness of our approach with several convolutional neural networks with varying number of layers and structures. When applied to the MNIST optical character recognition tasks, our approach achieves 99.52\% accuracy which significantly outperforms the state-of-the-art solutions and is very close to the accuracy of the best non-private version, 99.77\%. Also, it can make close to 164000 predictions per hour. We also applied our approach to CIFAR-10, which is much more complex compared to MNIST, and were able to achieve 91.5\% accuracy with approximation polynomials used as activation functions. These results show that CryptoDL provides efficient, accurate and scalable privacy-preserving predictions.

Connected through hyperedge HE-1 (induced by source paper "crypto oriented neural architecture design"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "xonn xnor based oblivious deep neural network inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "security and privacy issues in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-3 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "on device federated learning via blockchain and its latency analysis"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "securing distributed machine learning in high dimensions"
    - "entity resolution and federated learning get a federated resolution"
    - "how to backdoor federated learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "federated meta learning for recommendation"

Connected through hyperedge HE-4 (induced by source paper "multilinear compressive learning"):
  with members:
    - "compressed domain image classification using a multi rate neural network"
    - "compressively sensed image recognition"
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "tensor train recurrent neural networks for video classification"
    - "multilinear class specific discriminant analysis"
    - "tensor representation in high frequency financial data for price change prediction"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (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"
    - "the alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"

Connected through hyperedge HE-6 (induced by source paper "obfuscation for privacy preserving syntactic parsing"):
  with members:
    - "generalised differential privacy for text document processing"
    - "allennlp a deep semantic natural language processing platform"
    - "privacy preserving neural representations of text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial removal of demographic attributes from text data"
    - "towards robust and privacy preserving text representations"
    - "attention is all you need"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving hierarchical clustering formal security and efficient approximation"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "privpy enabling scalable and general privacy preserving computation"
    - "deepsecure scalable provably secure deep learning"
    - "a berkeley view of systems challenges for ai"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "privlogit efficient privacy preserving logistic regression by tailoring numerical optimizers"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-8 (induced by source paper "autogan based dimension reduction for privacy preservation"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "generative adversarial privacy"
    - "understanding compressive adversarial privacy"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "continuous control with deep reinforcement learning"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"
    - "functional mechanism regression analysis under differential privacy"

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)