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: "ngraph he a graph compiler for deep learning on homomorphically encrypted data"
  Abstract: Homomorphic encryption (HE)--the ability to perform computations on encrypted data--is an attractive remedy to increasing concerns about data privacy in the field of machine learning. However, building models that operate on ciphertext is currently labor-intensive and requires simultaneous expertise in deep learning, cryptography, and software engineering. Deep learning frameworks, together with recent advances in graph compilers, have greatly accelerated the training and deployment of deep learning models to a variety of computing platforms. Here, we introduce nGraph-HE, an extension of the nGraph deep learning compiler, which allows data scientists to deploy trained models with popular frameworks like TensorFlow, MXNet and PyTorch directly, while simply treating HE as another hardware target. This combination of frameworks and graph compilers greatly simplifies the development of privacy-preserving machine learning systems, provides a clean abstraction barrier between deep learning and HE, allows HE libraries to exploit HE-specific graph optimizations, and comes at a low cost in runtime overhead versus native HE operations.

Connected through hyperedge HE-1 (induced by source paper "hark side of deep learning from grad student descent to automated machine learning"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "evaluating the search phase of neural architecture search"
    - "do imagenet classifiers generalize to imagenet"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "regularized evolution for image classifier architecture search"
    - "universal approximations of invariant maps by neural networks"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "universality of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "low latency privacy preserving inference"):
  with members:
    - "the alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
    - "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)