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 "adversarial privacy preservation under attribute inference attack"):
  Abstract: With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges. Existing work focusing on protecting against membership inference attacks under the rigorous framework of differential privacy are vulnerable to attribute inference attacks. In light of the current gap between theory and practice, we develop a novel theoretical framework for privacy-preservation under the attack of attribute inference. Under our framework, we propose a minimax optimization formulation to protect the given attribute and analyze its privacy guarantees against arbitrary adversaries. On the other hand, it is clear that privacy constraint may cripple utility when the protected attribute is correlated with the target variable. To this end, we also prove an information-theoretic lower bound to precisely characterize the fundamental trade-off between utility and privacy. Empirically, we extensively conduct experiments to corroborate our privacy guarantee and validate the inherent trade-offs in different privacy preservation algorithms. Our experimental results indicate that the adversarial representation learning approaches achieve the best trade-off in terms of privacy preservation and utility maximization.

Includes members:
  Member "on learning invariant representation for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adaptation with conditional distribution matching and generalized label shift")
  Member "towards privacy preserving visual recognition via adversarial training a pilot study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "faster cryptonets leveraging sparsity for real world encrypted inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ensei efficient secure inference via frequency domain homomorphic convolution for privacy preserving visual recognition")
  Member "privacy preserving distributed deep learning for clinical data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "age progression regression by conditional adversarial autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "minimax filter learning to preserve privacy from inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "addressing target shift in zero shot learning using grouped adversarial learning")
  Member "equality of opportunity in supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithmic fairness")
  Member "you are who you know and how you behave attribute inference attacks via users social friends and behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")

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.LG(Machine Learning)