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: "secure computation for machine learning with spdz"
  Abstract: Secure Multi-Party Computation (MPC) is an area of cryptography that enables computation on sensitive data from multiple sources while maintaining privacy guarantees. However, theoretical MPC protocols often do not scale efficiently to real-world data. This project investigates the efficiency of the SPDZ framework, which provides an implementation of an MPC protocol with malicious security, in the context of popular machine learning (ML) algorithms. In particular, we chose applications such as linear regression and logistic regression, which have been implemented and evaluated using semi-honest MPC techniques. We demonstrate that the SPDZ framework outperforms these previous implementations while providing stronger security.

Connected through hyperedge HE-1 (induced by source paper "privacy for free communication efficient learning with differential privacy using sketches"):
  with members:
    - "towards federated learning at scale system design"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "communication efficient distributed sgd with sketching"
    - "federated learning challenges methods and future directions"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated heavy hitters discovery with differential privacy"
    - "enhancing the privacy of federated learning with sketching"
    - "differentially private meta learning"

Connected through hyperedge HE-2 (induced by source paper "harmonic coding an optimal linear code for privacy preserving gradient type computation"):
  with members:
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "speeding up distributed machine learning using codes"
    - "straggler mitigation in distributed matrix multiplication fundamental limits and optimal coding"
    - "on the optimal recovery threshold of coded matrix multiplication"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "short dot computing large linear transforms distributedly using coded short dot products"

Connected through hyperedge HE-3 (induced by source paper "privacy preserving blockchain mining sybil resistance by proof of useful work"):
  with members:
    - "a scalable verification solution for blockchains"
    - "dfinity technology overview series consensus system"
    - "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"
    - "consensus in the age of blockchains"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "bitcoin ng a scalable blockchain protocol"
    - "enigma decentralized computation platform with guaranteed privacy"
    - "bitcoin meets strong consistency"

Connected through hyperedge HE-4 (induced by source paper "disparate vulnerability on the unfairness of privacy attacks against machine learning"):
  with members:
    - "understanding membership inferences on well generalized learning models"
    - "xgboost a scalable tree boosting system"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "inherent trade offs in the fair determination of risk scores"
    - "concentrated differential privacy"
    - "membership inference attacks against machine learning models"
    - "calibrating data to sensitivity in private data analysis"
    - "fairness through awareness"

Connected through hyperedge HE-5 (induced by source paper "codedprivateml a fast and privacy preserving framework for distributed machine learning"):
  with members:
    - "private machine learning in tensorflow using secure computation"
    - "exploiting unintended feature leakage in collaborative learning"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "learning differentially private recurrent language models"
    - "cryptodl deep neural networks over encrypted data"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (induced by source paper "secure and efficient federated transfer learning"):
  with members:
    - "helen maliciously secure coopetitive learning for linear models"
    - "entity resolution and federated learning get a federated resolution"
    - "secure federated transfer learning"
    - "federated learning of deep networks using model averaging"
    - "membership inference attacks against machine learning models"

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)