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: "protecting sensory data against sensitive inferences"
  Abstract: There is growing concern about how personal data are used when users grant applications direct access to the sensors of their mobile devices. In fact, high resolution temporal data generated by motion sensors reflect directly the activities of a user and indirectly physical and demographic attributes. In this paper, we propose a feature learning architecture for mobile devices that provides flexible and negotiable privacy-preserving sensor data transmission by appropriately transforming raw sensor data. The objective is to move from the current binary setting of granting or not permission to an application, toward a model that allows users to grant each application permission over a limited range of inferences according to the provided services. The internal structure of each component of the proposed architecture can be flexibly changed and the trade-off between privacy and utility can be negotiated between the constraints of the user and the underlying application. We validated the proposed architecture in an activity recognition application using two real-world datasets, with the objective of recognizing an activity without disclosing gender as an example of private information. Results show that the proposed framework maintains the usefulness of the transformed data for activity recognition, with an average loss of only around three percentage points, while reducing the possibility of gender classification to around 50%, the target random guess, from more than 90% when using raw sensor data. We also present and distribute MotionSense, a new dataset for activity and attribute recognition collected from motion sensors.

Connected through hyperedge HE-1 (induced by source paper "multi task self supervised learning for human activity detection"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning for sensor based activity recognition a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "universal language model fine tuning for text classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "audio visual scene analysis with self supervised multisensory features"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "mobile sensor data anonymization"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "distributed one class learning"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "privacy preserving personal model training"
    - "deeprotect enabling inference based access control on mobile sensing applications"

Connected through hyperedge HE-3 (induced by source paper "privacy and utility preserving sensor data transformations"):
  with members:
    - "mobile sensor data anonymization"
    - "towards federated learning at scale system design"
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "context aware generative adversarial privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "learning human identity from motion patterns"
    - "censoring representations with an adversary"
    - "privacy against statistical inference"

Connected through hyperedge HE-4 (induced by source paper "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"):
  with members:
    - "formal limitations on the measurement of mutual information"
    - "information theoretic co training"
    - "representation learning with contrastive predictive coding"
    - "graph based predictable feature analysis"
    - "estimating mixture entropy with pairwise distances"
    - "unsupervised visual representation learning by context prediction"
    - "character aware neural language models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "zksense a privacy preserving mechanism for bot detection in mobile devices"):
  with members:
    - "batterylab a distributed power monitoring platform for mobile devices"
    - "touchsignatures identification of user touch actions and pins based on mobile sensor data via javascript"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "a hybrid deep learning architecture for privacy preserving mobile analytics"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "federated learning strategies for improving communication efficiency"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "deep learning in mobile and wireless networking a survey"
    - "deepsecure scalable provably secure deep learning"
    - "towards bottom up analysis of social food"
    - "inverting visual representations with convolutional networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

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)