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: "xonn xnor based oblivious deep neural network inference"
  Abstract: Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients. In this scenario, clients send their raw data to the server to run the deep learning model and send back the results. One standing challenge in this setting is to ensure the privacy of the clients' sensitive data. Oblivious inference is the task of running the neural network on the client's input without disclosing the input or the result to the server. This paper introduces XONN, a novel end-to-end framework based on Yao's Garbled Circuits (GC) protocol, that provides a paradigm shift in the conceptual and practical realization of oblivious inference. In XONN, the costly matrix-multiplication operations of the deep learning model are replaced with XNOR operations that are essentially free in GC. We further provide a novel algorithm that customizes the neural network such that the runtime of the GC protocol is minimized without sacrificing the inference accuracy. #R##N#We design a user-friendly high-level API for XONN, allowing expression of the deep learning model architecture in an unprecedented level of abstraction. Extensive proof-of-concept evaluation on various neural network architectures demonstrates that XONN outperforms prior art such as Gazelle (USENIX Security'18) by up to 7x, MiniONN (ACM CCS'17) by 93x, and SecureML (IEEE S&P'17) by 37x. State-of-the-art frameworks require one round of interaction between the client and the server for each layer of the neural network, whereas, XONN requires a constant round of interactions for any number of layers in the model. XONN is first to perform oblivious inference on Fitnet architectures with up to 21 layers, suggesting a new level of scalability compared with state-of-the-art. Moreover, we evaluate XONN on four datasets to perform privacy-preserving medical diagnosis.

Connected through hyperedge HE-1 (induced by source paper "nass optimizing secure inference via neural architecture search"):
  with members:
    - "video action recognition via neural architecture searching"
    - "hardware software co exploration of neural architectures"
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "on neural architecture search for resource constrained hardware platforms"
    - "device circuit architecture co exploration for computing in memory neural accelerators"
    - "prada protecting against dnn model stealing attacks"
    - "gazelle a low latency framework for secure neural network inference"

Connected through hyperedge HE-2 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition"):
  with members:
    - "privacy preserving image based localization"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "low latency privacy preserving inference"
    - "gazelle a low latency framework for secure neural network inference"
    - "stealing hyperparameters in machine learning"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "deepsecure scalable provably secure deep learning"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-3 (induced by source paper "optimizing privacy preserving outsourced convolutional neural network predictions"):
  with members:
    - "chiron privacy preserving machine learning as a service"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "gazelle a low latency framework for secure neural network inference"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "logan membership inference attacks against generative models"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-4 (induced by source paper "survey of attacks and defenses on edge deployed neural networks"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "bit flip attack crushing neural network with progressive bit search"
    - "deeplaser practical fault attack on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "security analysis of deep neural networks operating in the presence of cache side channel attacks"

Connected through hyperedge HE-5 (induced by source paper "secure evaluation of quantized neural networks"):
  with members:
    - "cryptflow secure tensorflow inference"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "training and inference with integers in deep neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "training deep neural networks with 8 bit floating point numbers"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"

Connected through hyperedge HE-6 (induced by source paper "crypto oriented neural architecture design"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "security and privacy issues in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"

Connected through hyperedge HE-7 (induced by source paper "cryptflow secure tensorflow inference"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "codedprivateml a fast and privacy preserving framework for distributed machine learning"
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "a hybrid approach to secure function evaluation using sgx"
    - "helen maliciously secure coopetitive learning for linear models"
    - "trident efficient 4pc framework for privacy preserving machine learning"
    - "astra high throughput 3pc over rings with application to secure prediction"
    - "outsourcing private machine learning via lightweight secure arithmetic computation"

Connected through hyperedge HE-8 (induced by source paper "quotient two party secure neural network training and prediction"):
  with members:
    - "mobile sensor data anonymization"
    - "on the convergence of adam and beyond"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "understanding batch normalization"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "signsgd compressed optimisation for non convex problems"

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)