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: "tapas tricks to accelerate encrypted prediction as a service"
  Abstract: Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the amount of computation and communication required between client and server. Fully homomorphic encryption offers a possible way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine ideas from the machine learning literature, particularly work on binarization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.

Connected through hyperedge HE-1 (induced by source paper "secure evaluation of quantized neural networks"):
  with members:
    - "cryptflow secure tensorflow inference"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "xonn xnor based oblivious deep neural network inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "training and inference with integers in deep neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "chameleon a hybrid secure computation framework for machine learning applications"

Connected through hyperedge HE-2 (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"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"

Connected through hyperedge HE-3 (induced by source paper "quotient two party secure neural network training and prediction"):
  with members:
    - "mobile sensor data anonymization"
    - "on the convergence of adam and beyond"
    - "xonn xnor based oblivious deep neural network inference"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "understanding batch normalization"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-4 (induced by source paper "xonn xnor based oblivious deep neural network inference"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "scalable and accurate deep learning for electronic health records"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "towards accurate binary convolutional neural network"
    - "rebnet residual binarized neural network"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-5 (induced by source paper "towards efficient and secure delivery of data for deep learning with privacy preserving"):
  with members:
    - "streaming end to end speech recognition for mobile devices"
    - "deep learning a critical appraisal"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "sealion a framework for neural network inference on encrypted data"):
  with members:
    - "private machine learning in tensorflow using secure computation"
    - "a generic framework for privacy preserving deep learning"
    - "chet compiler and runtime for homomorphic evaluation of tensor programs"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "emnist an extension of mnist to handwritten letters"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-7 (induced by source paper "she a fast and accurate privacy preserving deep neural network via leveled tfhe and logarithmic data representation"):
  with members:
    - "low latency privacy preserving inference"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "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-8 (induced by source paper "bayhenn combining bayesian deep learning and homomorphic encryption for secure dnn inference"):
  with members:
    - "gazelle a low latency framework for secure neural network inference"
    - "brain tumor segmentation with deep neural networks"
    - "ibm deep learning service"
    - "attention is all you need"
    - "efficient private erm for smooth objectives"
    - "a survey on deep learning in medical image analysis"
    - "deepsecure scalable provably secure deep learning"
    - "a survey on homomorphic encryption schemes theory and implementation"

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)