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: "a review of homomorphic encryption libraries for secure computation"
  Abstract: In this paper we provide a survey of various libraries for homomorphic encryption. We describe key features and trade-offs that should be considered while choosing the right approach for secure computation. We then present a comparison of six commonly available Homomorphic Encryption libraries - SEAL, HElib, TFHE, Paillier, ELGamal and RSA across these identified features. Support for different languages and real-life applications are also elucidated.

Connected through hyperedge HE-1 (induced by source paper "ngraph he a graph compiler for deep learning on homomorphically 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"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"

Connected through hyperedge HE-2 (induced by source paper "no peek a survey of private distributed deep learning"):
  with members:
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "how to backdoor federated learning"
    - "differentially private generative adversarial network"
    - "gazelle a low latency framework for secure neural network inference"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "cryptoimg privacy preserving processing over encrypted images"

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)