human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "privacy preserving hierarchical clustering formal security and efficient approximation"):
  Abstract: Machine Learning (ML) is widely used for predictive tasks in a number of critical applications. Recently, collaborative or federated learning is a new paradigm that enables multiple parties to jointly learn ML models on their combined datasets. Yet, in most application domains, such as healthcare and security analytics, privacy risks limit entities to individually learning local models over the sensitive datasets they own. In this work, we present the first formal study for privacy-preserving collaborative hierarchical clustering, overall featuring scalable cryptographic protocols that allow two parties to privately compute joint clusters on their combined sensitive datasets. First, we provide a formal definition that balances accuracy and privacy, and we present a provably secure protocol along with an optimized version for single linkage clustering. Second, we explore the integration of our protocol with existing approximation algorithms for hierarchical clustering, resulting in a protocol that can efficiently scale to very large datasets. Finally, we provide a prototype implementation and experimentally evaluate the feasibility and efficiency of our approach on synthetic and real datasets, with encouraging results. For example, for a dataset of one million records and 10 dimensions, our optimized privacy-preserving approximation protocol requires 35 seconds for end-to-end execution, just 896KB of communication, and achieves 97.09% accuracy.

Includes members:
  Member "chameleon a hybrid secure computation framework for machine learning applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cryptanalytic extraction of neural network models")
  Member "gazelle a low latency framework for secure neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep quaternion features for privacy protection")
  Member "privpy enabling scalable and general privacy preserving computation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private two party cluster analysis made formal scalable")
  Member "cryptodl deep neural networks over encrypted data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crypto oriented neural architecture design")
  Member "deepsecure scalable provably secure deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "a berkeley view of systems challenges for ai":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "large scale discrete fourier transform on tpus")
  Member "deep models under the gan information leakage from collaborative deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "privlogit efficient privacy preserving logistic regression by tailoring numerical optimizers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "high performance logistic regression for privacy preserving genome analysis")

Question: Please tell me which class the source paper 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)