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: "c sanitized a privacy model for document redaction and sanitization"
  Abstract: Within the current context of Information Societies, large amounts of information are daily exchanged and/or released. The sensitive nature of much of this information causes a serious privacy threat when documents are uncontrollably made available to untrusted third parties. In such cases, appropriate data protection measures should be undertaken by the responsible organization, especially under the umbrella of current legislations on data privacy. To do so, human experts are usually requested to redact or sanitize document contents. To relieve this burdensome task, this paper presents a privacy model for document redaction/sanitization, which offers several advantages over other models available in the literature. Based on the well-established foundations of data semantics and the information theory, our model provides a framework to develop and implement automated and inherently semantic redaction/sanitization tools. Moreover, contrary to ad-hoc redaction methods, our proposal provides a priori privacy guarantees which can be intuitively defined according to current legislations on data privacy. Empirical tests performed within the context of several use cases illustrate the applicability of our model and its ability to mimic the reasoning of human sanitizers.

Connected through hyperedge HE-1 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text"):
  with members:
    - "poincare wasserstein autoencoder"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "privacy preserving active learning on sensitive data for user intent classification"
    - "generalised differential privacy for text document processing"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "poincar e glove hyperbolic word embeddings"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-2 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations"):
  with members:
    - "auditing data provenance in text generation models"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "technical privacy metrics a systematic survey"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"
    - "plausible deniability for privacy preserving data synthesis"
    - "enriching word vectors with subword information"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "generalised differential privacy for text document processing"):
  with members:
    - "privacy preserving neural representations of text"
    - "the case for being average a mediocrity approach to style masking and author obfuscation"
    - "authorship verification based on compression models"
    - "learning differentially private recurrent language models"
    - "a comparison of neural models for word ordering"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "geo indistinguishability differential privacy for location based systems"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving data outsourcing in the cloud via semantic data splitting"):
  with members:
    - "toward sensitive document release with privacy guarantees"
    - "privacy driven access control in social networks by means of automatic semantic annotation"
    - "enforcing transparent access to private content in social networks by means of automatic sanitization"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-5 (induced by source paper "toward sensitive document release with privacy guarantees"):
  with members:
    - "fighting authorship linkability with crowdsourcing"
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-6 (induced by source paper "enforcing transparent access to private content in social networks by means of automatic sanitization"):
  with members:
    - "the anatomy of the facebook social graph"
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-7 (induced by source paper "ontology based access control in open scenarios applications to social networks and the cloud"):
  with members:
    - "utility preserving differentially private data releases via individual ranking microaggregation"
    - "privacy driven access control in social networks by means of automatic semantic annotation"

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)