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: "membership inference attacks on sequence to sequence models"
  Abstract: Data privacy is an important issue for "machine learning as a service" providers. We focus on the problem of membership inference attacks: given a data sample and black-box access to a model's API, determine whether the sample existed in the model's training data. Our contribution is an investigation of this problem in the context of sequence-to-sequence models, which are important in applications such as machine translation and video captioning. We define the membership inference problem for sequence generation, provide an open dataset based on state-of-the-art machine translation models, and report initial results on whether these models leak private information against several kinds of membership inference attacks.

Connected through hyperedge HE-1 (induced by source paper "data and model dependencies of membership inference attack"):
  with members:
    - "gan leaks a taxonomy of membership inference attacks against gans"
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "towards demystifying membership inference attacks"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "algorithms that remember model inversion attacks and data protection law"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-2 (induced by source paper "thieves on sesame street model extraction of bert based apis"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "high fidelity extraction of neural network models"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "learning and evaluating general linguistic intelligence"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "prediction poisoning utility constrained defenses against model stealing attacks"
    - "dawn dynamic adversarial watermarking of neural 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.LG(Machine Learning)