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: "discriminately decreasing discriminability with learned image filters"
  Abstract: In machine learning and computer vision, input images are often filtered to increase data discriminability. In some situations, however, one may wish to purposely decrease discriminability of one classification task (a "distractor" task), while simultaneously preserving information relevant to another (the task-of-interest): For example, it may be important to mask the identity of persons contained in face images before submitting them to a crowdsourcing site (e.g., Mechanical Turk) when labeling them for certain facial attributes. Another example is inter-dataset generalization: when training on a dataset with a particular covariance structure among multiple attributes, it may be useful to suppress one attribute while preserving another so that a trained classifier does not learn spurious correlations between attributes. In this paper we present an algorithm that finds optimal filters to give high discriminability to one task while simultaneously giving low discriminability to a distractor task. We present results showing the effectiveness of the proposed technique on both simulated data and natural face images.

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 "adversarial privacy preservation under attribute inference attack"):
  with members:
    - "on learning invariant representation for domain adaptation"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "faster cryptonets leveraging sparsity for real world encrypted inference"
    - "privacy preserving distributed deep learning for clinical data"
    - "age progression regression by conditional adversarial autoencoder"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "equality of opportunity in supervised learning"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial privacy"):
  with members:
    - "deeprotect enabling inference based access control on mobile sensing applications"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "minimax filter learning to preserve privacy from inference attacks"):
  with members:
    - "learning privately from multiparty data"
    - "calibrating data to sensitivity in private data analysis"
    - "privacy against statistical inference"
    - "differential privacy on the trade off between utility and information leakage"
    - "an information theoretic approach to privacy"
    - "differential privacy via wavelet transforms"
    - "what can we learn privately"

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.CV(Computer Vision and Pattern Recognition)