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: "a marauder s map of security and privacy in machine learning"
  Abstract: There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding. In this talk, we explore the threat model space of ML algorithms through the lens of Saltzer and Schroeder's principles for the design of secure computer systems. This characterization of the threat space prompts an investigation of current and future research directions. We structure our discussion around three of these directions, which we believe are likely to lead to significant progress. The first encompasses a spectrum of approaches to verification and admission control, which is a prerequisite to enable fail-safe defaults in machine learning systems. The second seeks to design mechanisms for assembling reliable records of compromise that would help understand the degree to which vulnerabilities are exploited by adversaries, as well as favor psychological acceptability of machine learning applications. The third pursues formal frameworks for security and privacy in machine learning, which we argue should strive to align machine learning goals such as generalization with security and privacy desiderata like robustness or privacy. Key insights resulting from these three directions pursued both in the ML and security communities are identified and the effectiveness of approaches are related to structural elements of ML algorithms and the data used to train them. We conclude by systematizing best practices in our community.

Connected through hyperedge HE-1 (induced by source paper "adversarial machine learning industry perspectives"):
  with members:
    - "the deepfake detection challenge dfdc preview dataset"
    - "secml a python library for secure and explainable machine learning"
    - "failure modes in machine learning systems"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial music real world audio adversary against wake word detection system"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "adversarial reprogramming of neural networks"

Connected through hyperedge HE-2 (induced by source paper "privacy enhanced multimodal neural representations for emotion recognition"):
  with members:
    - "mirroring to build trust in digital assistants"
    - "differential privacy has disparate impact on model accuracy"
    - "racial bias in hate speech and abusive language detection datasets"
    - "muse ing on the impact of utterance ordering on crowdsourced emotion annotations"
    - "adversarial task specific privacy preservation under attribute attack"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "privacy preserving neural representations of text"
    - "the priori emotion dataset linking mood to emotion detected in the wild"

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)