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: "deeprotect enabling inference based access control on mobile sensing applications"
  Abstract: Personal sensory data is used by context-aware mobile applications to provide utility. However, the same data can also be used by an adversary to make sensitive inferences about a user thereby violating her privacy. #R##N#We present DEEProtect, a framework that enables a novel form of inference control, in which mobile apps with access to sensor data are limited (provably) in their ability to make inferences about user's sensitive data and behavior. DEEProtect adopts a two-layered privacy strategy. First, it leverages novel autoencoder techniques to perform data minimization and limits the amount of information being shared; the learning network is used to derive a compact representation of sensor data consisting only of features relevant to authorized utility-providing inferences. Second, DEEProtect obfuscates the previously learnt features, thereby providing an additional layer of protection against sensitive inferences. Our framework supports both conventional as well as a novel relaxed notion of local differential privacy that enhances utility. Through theoretical analysis and extensive experiments using real-world datasets, we demonstrate that when compared to existing approaches DEEProtect provides provable privacy guarantees with up to 8x improvement in utility. Finally, DEEProtect shares obfuscated but raw sensor data reconstructed from the perturbed features, thus requiring no changes to the existing app interfaces.

Connected through hyperedge HE-1 (induced by source paper "learning generative adversarial representations gap under fairness and censoring constraints"):
  with members:
    - "flexibly fair representation learning by disentanglement"
    - "distributed generation of privacy preserving data with user customization"
    - "privacy under hard distortion constraints"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairness in supervised learning an information theoretic approach"
    - "fairgan fairness aware generative adversarial networks"
    - "learning to protect communications with adversarial neural cryptography"

Connected through hyperedge HE-2 (induced by source paper "distributed generation of privacy preserving data with user customization"):
  with members:
    - "understanding compressive adversarial privacy"
    - "protecting sensitive attributes via generative adversarial networks"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "learning to protect communications with adversarial neural cryptography"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-3 (induced by source paper "collecting and analyzing multidimensional data with local differential privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "optimal schemes for discrete distribution estimation under locally differential privacy"
    - "clustering algorithms for the centralized and local models"
    - "heavy hitters and the structure of local privacy"

Connected through hyperedge HE-4 (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"
    - "protecting sensory data against sensitive inferences"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "generative adversarial privacy"):
  with members:
    - "minimax filter learning to preserve privacy from inference attacks"
    - "deep variational information bottleneck"
    - "differentially private hypothesis testing revisited"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "censoring representations with an adversary"
    - "conditional generative adversarial nets"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "context aware generative adversarial privacy"):
  with members:
    - "estimation efficiency under privacy constraints"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "information theoretic privacy with general distortion constraints"
    - "ensemble estimation of mutual information"
    - "information potential auto encoders"
    - "privacy aware mmse estimation"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "deep variational information bottleneck"

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.CR(Cryptography and Security)