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: "privado practical and secure dnn inference"
  Abstract: Recently, cloud providers have extended support for trusted hardware primitives such as Intel SGX. Simultaneously, the field of deep learning is seeing enormous innovation and increase in adoption. In this paper, we therefore ask the question: "Can third-party cloud services use SGX to provide practical, yet secure DNN Inference-as-a-service? " Our work addresses the three main challenges that SGX-based DNN inferencing faces, namely, security, ease-of-use, and performance. #R##N#We first demonstrate that side-channel based attacks on DNN models are indeed possible. We show that, by observing access patterns, we can recover inputs to the DNN model. This motivates the need for Privado, a system we have designed for secure inference-as-a-service. Privado is input-oblivious: it transforms any deep learning framework written in C/C++ to be free of input-dependent access patterns. Privado is fully-automated and has a low TCB: with zero developer effort, given an ONNX description, it generates compact C code for the model which can run within SGX-enclaves. Privado has low performance overhead: we have used Privado with Torch, and have shown its overhead to be 20.77\% on average on 10 contemporary networks.

Connected through hyperedge HE-1 (induced by source paper "mind your weight s a large scale study on insufficient machine learning model protection in mobile apps"):
  with members:
    - "high fidelity extraction of neural network models"
    - "on device neural net inference with mobile gpus"
    - "tensorscone a secure tensorflow framework using intel sgx"
    - "knockoff nets stealing functionality of black box models"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "a first look at deep learning apps on smartphones"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-2 (induced by source paper "quantifying hyper parameter leakage in machine learning"):
  with members:
    - "prediction poisoning towards defenses against dnn model stealing attacks"
    - "a tutorial on learning with bayesian networks"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "f bleau fast black box leakage estimation"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "exploring connections between active learning and model extraction"

Connected through hyperedge HE-3 (induced by source paper "maskednet the first hardware inference engine aiming power side channel protection"):
  with members:
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "csi neural network using side channels to recover your artificial neural network information"
    - "prada protecting against dnn model stealing attacks"
    - "stealing hyperparameters in machine learning"
    - "finn a framework for fast scalable binarized neural network inference"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "keystone an open framework for architecting tees"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "zombieload cross privilege boundary data sampling"
    - "practical verifiable in network filtering for ddos defense"
    - "sanctorum a lightweight security monitor for secure enclaves"
    - "spectre attacks exploiting speculative execution"
    - "besfs mechanized proof of an iago safe filesystem for enclaves"
    - "mi6 secure enclaves in a speculative out of order processor"
    - "teechan payment channels using trusted execution environments"

Connected through hyperedge HE-5 (induced by source paper "confidential deep learning executing proprietary models on untrusted devices"):
  with members:
    - "towards characterizing and limiting information exposure in dnn layers"
    - "benchmark analysis of representative deep neural network architectures"
    - "branchynet fast inference via early exiting from deep neural networks"

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)