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: "securing input data of deep learning inference systems via partitioned enclave execution"
  Abstract: Deep learning systems have been widely deployed as backend engines of artificial intelligence (AI) services for their approaching-human performance in cognitive tasks. However, end users always have some concerns about the confidentiality of their provisioned input data, even for those reputable AI service providers. Accidental disclosures of sensitive user data might unexpectedly happen due to security breaches, exploited vulnerabilities, neglect, or insiders. In this paper, we systematically investigate the potential information exposure in deep learning based AI inference systems. Based on our observation, we develop DeepEnclave, a privacy-enhancing system to mitigate sensitive information disclosure in deep learning inference pipelines. The key innovation is to partition deep learning models and leverage secure enclave techniques on cloud infrastructures to cryptographically protect the confidentiality and integrity of user inputs. We formulate the information exposure problem as a reconstruction privacy attack and quantify the adversary's capabilities with different attack strategies. Our comprehensive security analysis and performance measurement can act as a guideline for end users to determine their principle of partitioning deep neural networks, thus to achieve maximum privacy guarantee with acceptable performance overhead.

Connected through hyperedge HE-1 (induced by source paper "industrial scale privacy preserving deep neural network"):
  with members:
    - "privacy preserving pca for multiparty modeling"
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "secure social recommendation based on secret sharing"
    - "practical privacy preserving poi recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "distributed learning of deep neural network over multiple agents"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving inference in machine learning services using trusted execution environments"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "towards characterizing and limiting information exposure in dnn layers"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "chiron privacy preserving machine learning as a service"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "a first look at deep learning apps on smartphones"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "deepcache principled cache for mobile deep vision"
    - "digital watermarking for deep neural networks"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "understanding diverse usage patterns from large scale appstore service profiles"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "reaching data confidentiality and model accountability on the caltrain"):
  with members:
    - "contamination attacks and mitigation in multi party machine learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "chiron privacy preserving machine learning as a service"
    - "how to backdoor federated learning"
    - "efficient deep learning on multi source private data"
    - "mitigating sybils in federated learning poisoning"
    - "the circle game scalable private membership test using trusted hardware"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

Connected through hyperedge HE-6 (induced by source paper "mlcapsule guarded offline deployment of machine learning as a service"):
  with members:
    - "tagvisor a privacy advisor for sharing hashtags"
    - "chiron privacy preserving machine learning as a service"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "understanding and controlling user linkability in decentralized learning"
    - "prada protecting against dnn model stealing attacks"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"
    - "sok security and privacy in machine learning"

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)