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: "use privacy in data driven systems theory and experiments with machine learnt programs"
  Abstract: This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.

Connected through hyperedge HE-1 (induced by source paper "quantitative verification of neural networks and its security applications"):
  with members:
    - "phase transition behavior of cardinality and xor constraints"
    - "adversarial examples are a natural consequence of test error in noise"
    - "strip a defence against trojan attacks on deep neural networks"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "adversarial spheres"

Connected through hyperedge HE-2 (induced by source paper "perfectly parallel fairness certification of neural networks"):
  with members:
    - "probabilistic verification of fairness properties via concentration"
    - "a provable defense for deep residual networks"
    - "a survey on bias and fairness in machine learning"
    - "analyzing deep neural networks with symbolic propagation towards higher precision and faster verification"
    - "the efficacy of shield under different threat models"
    - "deepsearch simple and effective blackbox fuzzing of deep neural networks"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "fliptest fairness testing via optimal transport"):
  with members:
    - "adversarial computation of optimal transport maps"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "fairness under composition"
    - "scalable unbalanced optimal transport using generative adversarial networks"
    - "approximating optimal transport with linear programs"
    - "automated test generation to detect individual discrimination in ai models"
    - "hunting for discriminatory proxies in linear regression models"
    - "an empirical study of rich subgroup fairness for machine learning"

Connected through hyperedge HE-4 (induced by source paper "enforcing private data usage control with blockchain and attested off chain contract execution"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "private data objects an overview"
    - "chiron privacy preserving machine learning as a service"
    - "sok sanitizing for security"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "enhancing bitcoin security and performance with strong consistency via collective signing"
    - "enigma decentralized computation platform with guaranteed privacy"

Connected through hyperedge HE-5 (induced by source paper "racial categories in machine learning"):
  with members:
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "discrimination in online ad delivery"
    - "fairness through awareness"

Connected through hyperedge HE-6 (induced by source paper "hunting for discriminatory proxies in linear regression models"):
  with members:
    - "proxy non discrimination in data driven systems"
    - "fairness through awareness"

Connected through hyperedge HE-7 (induced by source paper "a characterization of monotone influence measures for data classification"):
  with members:
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "statistical cost sharing"
    - "challenges for transparency"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "proxy non discrimination in data driven systems"
    - "why should i trust you explaining the predictions of any classifier"
    - "learning cooperative games"

Connected through hyperedge HE-8 (induced by source paper "proxy non discrimination in data driven systems"):
  with members:
    - "discovering unwarranted associations in data driven applications with the fairtest testing toolkit"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "a decision theoretic approach to targeted advertising"
    - "discrimination in online ad delivery"
    - "fairness through awareness"

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)