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: "privpy enabling scalable and general privacy preserving computation"
  Abstract: We introduce PrivPy, a practical privacy-preserving collaborative computation framework. PrivPy provides an easy-to-use and highly compatible Python programming front-end which supports high-level array operations and different secure computation engines to allow for security assumptions and performance trade-offs. We also design and implement a new secret-sharing-based computation engine with highly efficient protocols for private arithmetics over real numbers: a fast secure multiplication protocol, a garbled-circuit-based secure comparison protocol, and optimized array/matrix operations that are essential for big data applications. PrivPy provides provable privacy and supports general computation. We demonstrate the scalability of PrivPy using machine learning models (e.g. logistic regression and convolutional neural networks) and real-world datasets (including a 5000-by-1-million private matrix).

Connected through hyperedge HE-1 (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"
    - "cryptodl deep neural networks over encrypted data"
    - "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-2 (induced by source paper "private two party cluster analysis made formal scalable"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "cryptodl deep neural networks over encrypted data"
    - "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"
    - "calibrating data to sensitivity in private data analysis"

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)