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: "faster cryptonets leveraging sparsity for real world encrypted inference"
  Abstract: Homomorphic encryption enables arbitrary computation over data while it remains encrypted. This privacy-preserving feature is attractive for machine learning, but requires significant computational time due to the large overhead of the encryption scheme. We present Faster CryptoNets, a method for efficient encrypted inference using neural networks. We develop a pruning and quantization approach that leverages sparse representations in the underlying cryptosystem to accelerate inference. We derive an optimal approximation for popular activation functions that achieves maximally-sparse encodings and minimizes approximation error. We also show how privacy-safe training techniques can be used to reduce the overhead of encrypted inference for real-world datasets by leveraging transfer learning and differential privacy. Our experiments show that our method maintains competitive accuracy and achieves a significant speedup over previous methods. This work increases the viability of deep learning systems that use homomorphic encryption to protect user privacy.

Connected through hyperedge HE-1 (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"
    - "xonn xnor based oblivious deep neural network inference"
    - "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"
    - "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-2 (induced by source paper "heaan demystified accelerating fully homomorphic encryption through architecture centric analysis and optimization"):
  with members:
    - "ngraph he2 a high throughput framework for neural network inference on encrypted data"
    - "heax high performance architecture for computation on homomorphically encrypted data in the cloud"
    - "the alexnet moment for homomorphic encryption hcnn the first homomorphic cnn on encrypted data with gpus"
    - "faster arithmetic for number theoretic transforms"

Connected through hyperedge HE-3 (induced by source paper "crypto oriented neural architecture design"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "xonn xnor based oblivious deep neural network inference"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "adversarial task specific privacy preservation under attribute attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning adversarially fair and transferable representations"
    - "towards robust and privacy preserving text representations"
    - "privacy preserving distributed deep learning for clinical data"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"

Connected through hyperedge HE-5 (induced by source paper "xonn xnor based oblivious deep neural network inference"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "scalable and accurate deep learning for electronic health records"
    - "gazelle a low latency framework for secure neural network inference"
    - "towards accurate binary convolutional neural network"
    - "rebnet residual binarized neural network"
    - "deepsecure scalable provably secure deep learning"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-6 (induced by source paper "cheetah an ultra fast approximation free and privacy preserved neural network framework based on joint obscure linear and nonlinear computations"):
  with members:
    - "xonn xnor based oblivious deep neural network inference"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "falcon a fourier transform based approach for fast and secure convolutional neural network predictions"
    - "gazelle a low latency framework for secure neural network inference"
    - "stealing hyperparameters in machine learning"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "machine learning models that remember too much"
    - "deepsecure scalable provably secure deep learning"

Connected through hyperedge HE-7 (induced by source paper "sealion a framework for neural network inference on encrypted data"):
  with members:
    - "private machine learning in tensorflow using secure computation"
    - "a generic framework for privacy preserving deep learning"
    - "chet compiler and runtime for homomorphic evaluation of tensor programs"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "gazelle a low latency framework for secure neural network inference"
    - "emnist an extension of mnist to handwritten letters"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-8 (induced by source paper "adversarial privacy preservation under attribute inference attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "privacy preserving distributed deep learning for clinical data"
    - "age progression regression by conditional adversarial autoencoder"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "equality of opportunity in supervised learning"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"
    - "wide residual 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)