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 "mobile sensor data anonymization"):
  Abstract: Motion sensors such as accelerometers and gyroscopes measure the instant acceleration and rotation of a device, in three dimensions. Raw data streams from motion sensors embedded in portable and wearable devices may reveal private information about users without their awareness. For example, motion data might disclose the weight or gender of a user, or enable their re-identification. To address this problem, we propose an on-device transformation of sensor data to be shared for specific applications, such as monitoring selected daily activities, without revealing information that enables user identification. We formulate the anonymization problem using an information-theoretic approach and propose a new multi-objective loss function for training deep autoencoders. This loss function helps minimizing user-identity information as well as data distortion to preserve the application-specific utility. The training process regulates the encoder to disregard user-identifiable patterns and tunes the decoder to shape the output independently of users in the training set. The trained autoencoder can be deployed on a mobile or wearable device to anonymize sensor data even for users who are not included in the training dataset. Data from 24 users transformed by the proposed anonymizing autoencoder lead to a promising trade-off between utility and privacy, with an accuracy for activity recognition above 92% and an accuracy for user identification below 7%.

Includes members:
  Member "replacement autoencoder a privacy preserving algorithm for sensory data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy and utility preserving sensor data transformations")
  Member "optimal utility privacy trade off with the total variation distance as the privacy measure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tunable measures for information leakage and applications to privacy utility tradeoffs")
  Member "distributed one class learning":
    also connected through other hyperedges:
      (none)
  Member "protecting sensory data against sensitive inferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task self supervised learning for human activity detection")
  Member "privacy loss in apple s implementation of differential privacy on macos 10 12":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "privacy preserving adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints")
  Member "context aware generative adversarial privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "privacy preserving personal model training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")

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)