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: "chameleon a hybrid secure computation framework for machine learning applications"
  Abstract: We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of generic SFE protocols with the ones that are based upon additive secret sharing. In particular, the framework performs linear operations in the ring $\mathbb{Z}_{2^l}$ using additively secret shared values and nonlinear operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson protocol. Chameleon departs from the common assumption of additive or linear secret sharing models where three or more parties need to communicate in the online phase: the framework allows two parties with private inputs to communicate in the online phase under the assumption of a third node generating correlated randomness in an offline phase. Almost all of the heavy cryptographic operations are precomputed in an offline phase which substantially reduces the communication overhead. Chameleon is both scalable and significantly more efficient than the ABY framework (NDSS'15) it is based on. Our framework supports signed fixed-point numbers. In particular, Chameleon's vector dot product of signed fixed-point numbers improves the efficiency of mining and classification of encrypted data for algorithms based upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer convolutional deep neural network shows 133x and 4.2x faster executions than Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively.

Connected through hyperedge HE-1 (induced by source paper "cryptanalytic extraction of neural network models"):
  with members:
    - "high fidelity extraction of neural network models"
    - "thieves on sesame street model extraction of bert based apis"
    - "analyzing and improving the image quality of stylegan"
    - "identifying weights and architectures of unknown relu networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "exploring connections between active learning and model extraction"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-2 (induced by source paper "federated machine learning with anonymous random hybridization fearh on medical records"):
  with members:
    - "federated machine learning concept and applications"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "secure evaluation of quantized neural networks"):
  with members:
    - "cryptflow secure tensorflow inference"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "xonn xnor based oblivious deep neural network inference"
    - "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"
    - "tapas tricks to accelerate encrypted prediction as a service"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "xonn xnor based oblivious deep neural network inference"

Connected through hyperedge HE-6 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "on device federated learning via blockchain and its latency analysis"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "securing distributed machine learning in high dimensions"
    - "entity resolution and federated learning get a federated resolution"
    - "how to backdoor federated learning"
    - "federated meta learning for recommendation"
    - "federated learning with non iid data"

Connected through hyperedge HE-7 (induced by source paper "xonn xnor based oblivious deep neural network inference"):
  with members:
    - "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"
    - "faster cryptonets leveraging sparsity for real world encrypted 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-8 (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"
    - "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"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "generating adversarial malware examples for black box attacks based on gan"
    - "machine learning models that remember too much"
    - "deepsecure scalable provably secure deep 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)