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: "helen maliciously secure coopetitive learning for linear models"
  Abstract: Many organizations wish to collaboratively train machine learning models on their combined datasets for a common benefit (e.g., better medical research, or fraud detection). However, they often cannot share their plaintext datasets due to privacy concerns and/or business competition. In this paper, we design and build Helen, a system that allows multiple parties to train a linear model without revealing their data, a setting we call coopetitive learning. Compared to prior secure training systems, Helen protects against a much stronger adversary who is malicious and can compromise m-1 out of m parties. Our evaluation shows that Helen can achieve up to five orders of magnitude of performance improvement when compared to training using an existing state-of-the-art secure multi-party computation framework.

Connected through hyperedge HE-1 (induced by source paper "cryptflow secure tensorflow inference"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "a hybrid approach to secure function evaluation using sgx"
    - "trident efficient 4pc framework for privacy preserving machine learning"
    - "astra high throughput 3pc over rings with application to secure prediction"
    - "xonn xnor based oblivious deep neural network inference"
    - "outsourcing private machine learning via lightweight secure arithmetic computation"

Connected through hyperedge HE-2 (induced by source paper "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"):
  with members:
    - "evaluating differentially private machine learning in practice"
    - "hotstuff bft consensus in the lens of blockchain"
    - "dp admm admm based distributed learning with differential privacy"
    - "gazelle a low latency framework for secure neural network inference"
    - "plausible deniability for privacy preserving data synthesis"
    - "differentially private federated learning a client level perspective"
    - "towards practical differential privacy for sql queries"
    - "smcql secure querying for federated databases"

Connected through hyperedge HE-3 (induced by source paper "secure and efficient federated transfer learning"):
  with members:
    - "secure computation for machine learning with spdz"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "truthful and faithful monetary policy for a stablecoin conducted by a decentralised encrypted artificial intelligence"):
  with members:
    - "how to make a digital currency on a blockchain stable"
    - "a proximal dual consensus admm method for multi agent constrained optimization"

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)