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: "privacy partitioning protecting user data during the deep learning inference phase"
  Abstract: We present a practical method for protecting data during the inference phase of deep learning based on bipartite topology threat modeling and an interactive adversarial deep network construction. We term this approach \emph{Privacy Partitioning}. In the proposed framework, we split the machine learning models and deploy a few layers into users' local devices, and the rest of the layers into a remote server. We propose an approach to protect user's data during the inference phase, while still achieve good classification accuracy. #R##N#We conduct an experimental evaluation of this approach on benchmark datasets of three computer vision tasks. The experimental results indicate that this approach can be used to significantly attenuate the capacity for an adversary with access to the state-of-the-art deep network's intermediate states to learn privacy-sensitive inputs to the network. For example, we demonstrate that our approach can prevent attackers from inferring the private attributes such as gender from the Face image dataset without sacrificing the classification accuracy of the original machine learning task such as Face Identification.

Connected through hyperedge HE-1 (induced by source paper "overlearning reveals sensitive attributes"):
  with members:
    - "similarity of neural network representations revisited"
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "exploiting unintended feature leakage in collaborative learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "privacy preserving neural representations of text"
    - "adversarial removal of demographic attributes from text data"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-2 (induced by source paper "adversarial task specific privacy preservation under attribute attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning adversarially fair and transferable representations"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "towards robust and privacy preserving text representations"
    - "privacy preserving distributed deep learning for clinical data"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "on fairness and calibration"

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)