human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards probabilistic verification of machine unlearning"):
  Abstract: Right to be forgotten, also known as the right to erasure, is the right of individuals to have their data erased from an entity storing it. The General Data Protection Regulation in the European Union legally solidified the status of this long held notion. As a consequence, there is a growing need for the development of mechanisms whereby users can verify if service providers comply with their deletion requests. #R##N#In this work, we take the first step in proposing a formal framework to study the design of such verification mechanisms for data deletion requests -- also known as machine unlearning -- in the context of systems that provide machine learning as a service. We propose a backdoor-based verification mechanism and demonstrate its effectiveness in certifying data deletion with high confidence using the above framework. Our mechanism makes a novel use of backdoor attacks in ML as a basis for quantitatively inferring machine unlearning. In our mechanism, each user poisons part of its training data by injecting a user-specific backdoor trigger associated with a user-specific target label. The prediction of target labels on test samples with the backdoor trigger is then used as an indication of the user's data being used to train the ML model. #R##N#We formalize the verification process as a hypothesis testing problem, and provide theoretical guarantees on the statistical power of the hypothesis test. We experimentally demonstrate that our approach has minimal effect on the machine learning service but provides high confidence verification of unlearning. We show that with a $30\%$ poison ratio and merely $20$ test queries, our verification mechanism has both false positive and false negative ratios below $10^{-5}$. Furthermore, we also show the effectiveness of our approach by testing it against an adaptive adversary that uses a state-of-the-art backdoor defense method.

Includes members:
  Member "formalizing data deletion in the context of the right to be forgotten":
    also connected through other hyperedges:
      (none)
  Member "nnoculation broad spectrum and targeted treatment of backdoored dnns":
    also connected through other hyperedges:
      (none)
  Member "machine unlearning linear filtration for logit based classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations")
  Member "bypassing backdoor detection algorithms in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backdooring and poisoning neural networks with image scaling attacks")
  Member "hidden trigger backdoor attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "making ai forget you data deletion in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a distributional framework for data valuation")
  Member "certified data removal from machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations")
  Member "auditing data provenance in text generation models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative models for effective ml on private decentralized datasets")

Question: Please tell me which class the source paper 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)