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: "shredder learning noise distributions to protect inference privacy"
  Abstract: Sheer amount of computation in deep neural networks has pushed their execution to the cloud. This de facto cloud-hosted inference, however, raises serious privacy concerns as private data is communicated and stored in remote servers. The data could be mishandled by cloud providers, used for unsolicited analytics, or simply compromised through network and system security vulnerability. To that end, this paper devises SHREDDER that reduces the information content of the communicated data without diminishing the cloud's ability to maintain acceptably high accuracy. To that end, SHREDDER learns two sets of noise distributions whose samples, named multiplicative and additive noise tensors, are applied to the communicated data while maintaining the inference accuracy. The key idea is that SHREDDER learns these noise distributions offline without altering the topology or the weights of the pre-trained network. SHREDDER repeatedly learns sample noise tensors from the distributions by casting the tensors as a set of trainable parameters while keeping the weights constant. Since the key idea is learning the noise, we are able to devise a loss function that strikes a balance between accuracy and information degradation. To this end, we use self-supervision to train the noise tensors to achieve an intermediate representation of the data that contains less private information. Experimentation with real-world deep neural networks shows that, compared to the original execution, SHREDDER reduces the mutual information between the input and the communicated data by 66.90%, and yields a misclassification rate of 94.5% over private labels, significantly reducing adversary's ability to infer private data, while sacrificing only 1.74% loss in accuracy without any knowledge about the private labels.

Connected through hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey"):
  with members:
    - "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"
    - "stripe tensor compilation via the nested polyhedral model"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "privacy for rescue a new testimony why privacy is vulnerable in deep models"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "a comprehensive survey on graph neural networks"
    - "relay a high level compiler for deep learning"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving inference in machine learning services using trusted execution environments"):
  with members:
    - "partially encrypted machine learning using functional encryption"
    - "sgxpectre stealing intel secrets from sgx enclaves via speculative execution"
    - "x gans image reconstruction made easy for extreme cases"
    - "spectre attacks exploiting speculative execution"
    - "the alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "securing input data of deep learning inference systems via partitioned enclave execution"

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)