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: "machine learning models that remember too much"
  Abstract: Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. #R##N#We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. #R##N#We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data.

Connected through hyperedge HE-1 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models"):
  with members:
    - "strong baseline defenses against clean label poisoning attacks"
    - "anonymousnet natural face de identification with measurable privacy"
    - "hiding faces in plain sight disrupting ai face synthesis with adversarial perturbations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "advhat real world adversarial attack on arcface face id system"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-2 (induced by source paper "salvaging federated learning by local adaptation"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "advances and open problems in federated learning"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "towards federated learning at scale system design"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "federated evaluation of on device personalization"
    - "measuring the effects of non identical data distribution for federated visual classification"

Connected through hyperedge HE-3 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "thieves on sesame street model extraction of bert based apis"
    - "where is the information in a deep neural network"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "enhanced convolutional neural tangent kernels"
    - "on exact computation with an infinitely wide neural net"
    - "certified data removal from machine learning models"
    - "information in infinite ensembles of infinitely wide neural networks"

Connected through hyperedge HE-4 (induced by source paper "formalizing data deletion in the context of the right to be forgotten"):
  with members:
    - "machine unlearning linear filtration for logit based classifiers"
    - "eternal sunshine of the spotless net selective forgetting in deep networks"
    - "making ai forget you data deletion in machine learning"
    - "proving erasure"
    - "machine unlearning"
    - "algorithms that remember model inversion attacks and data protection law"

Connected through hyperedge HE-5 (induced by source paper "the differentially private lottery ticket mechanism"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "differentially private generative adversarial network"
    - "learning differentially private recurrent language models"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-6 (induced by source paper "updates leak data set inference and reconstruction attacks in online learning"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "diversity sensitive conditional generative adversarial networks"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-7 (induced by source paper "reconciling utility and membership privacy via knowledge distillation"):
  with members:
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "evaluating differentially private machine learning in practice"
    - "zero shot knowledge distillation in deep networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "private model compression via knowledge distillation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "i know what you see power side channel attack on convolutional neural network accelerators"

Connected through hyperedge HE-8 (induced by source paper "effectiveness of distillation attack and countermeasure on neural network watermarking"):
  with members:
    - "deep learning is robust to massive label noise"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "are you tampering with my data"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"

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)