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: "privlogit efficient privacy preserving logistic regression by tailoring numerical optimizers"
  Abstract: Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here, we make a novel observation that this is partially due to naive adoption of mainstream numerical optimization (e.g., Newton method) and failing to tailor for secure computing. This work presents a contrasting perspective: customizing numerical optimization specifically for secure settings. We propose a seemingly less-favorable optimization method that can in fact significantly accelerate privacy-preserving logistic regression. Leveraging this new method, we propose two new secure protocols for conducting logistic regression in a privacy-preserving and distributed manner. Extensive theoretical and empirical evaluations prove the competitive performance of our two secure proposals while without compromising accuracy or privacy: with speedup up to 2.3x and 8.1x, respectively, over state-of-the-art; and even faster as data scales up. Such drastic speedup is on top of and in addition to performance improvements from existing (and future) state-of-the-art cryptography. Our work provides a new way towards efficient and practical privacy-preserving logistic regression for large-scale studies which are common for modern science.

Connected through hyperedge HE-1 (induced by source paper "high performance logistic regression for privacy preserving genome analysis"):
  with members:
    - "a framework for efficient adaptively secure composable oblivious transfer in the rom"
    - "comparison among dimensionality reduction techniques based on random projection for cancer classification"

Connected through hyperedge HE-2 (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"
    - "privpy enabling scalable and general privacy preserving computation"
    - "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"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "private two party cluster analysis made formal scalable"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "privpy enabling scalable and general privacy preserving computation"
    - "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"
    - "membership inference attacks against machine learning models"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-4 (induced by source paper "carenets compact and resource efficient cnn for homomorphic inference on encrypted medical images"):
  with members:
    - "a generic framework for privacy preserving deep learning"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "the alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "cryptodl deep neural networks over encrypted data"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-5 (induced by source paper "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"):
  with members:
    - "loss factorization weakly supervised learning and label noise robustness"
    - "fast learning from distributed datasets without entity matching"
    - "rademacher observations private data and boosting"

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.LG(Machine Learning)