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: "from t closeness to differential privacy and vice versa in data anonymization"
  Abstract: k-anonymity and e-differential privacy are two mainstream privacy models, the former introduced to anonymize data sets and the latter to limit the knowledge gain that results from including one individual in the data set. Whereas basic k-anonymity only protects against identity disclosure, t-closeness was presented as an extension of k-anonymity that also protects against attribute disclosure. We show here that, if not quite equivalent, t-closeness and e-differential privacy are strongly related to one another when it comes to anonymizing data sets. Specifically, k-anonymity for the quasi-identifiers combined with e-differential privacy for the confidential attributes yields stochastic t-closeness (an extension of t-closeness), with t a function of k and e. Conversely, t-closeness can yield e-differential privacy when t=exp(e/2) and the assumptions made by t-closeness about the prior and posterior views of the data hold.

Connected through hyperedge HE-1 (induced by source paper "techniques d anonymisation tabulaire concepts et mise en oeuvre"):
  with members:
    - "when the signal is in the noise exploiting diffix s sticky noise"
    - "differentially private mixture of generative neural networks"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "publishing microdata with a robust privacy guarantee"

Connected through hyperedge HE-2 (induced by source paper "technical privacy metrics a systematic survey"):
  with members:
    - "evaluating the strength of genomic privacy metrics"
    - "private matchings and allocations"
    - "constructing elastic distinguishability metrics for location privacy"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "differential privacy an economic method for choosing epsilon"
    - "utility privacy tradeoffs in databases an information theoretic approach"

Connected through hyperedge HE-3 (induced by source paper "connecting randomized response post randomization differential privacy and t closeness via deniability and permutation"):
  with members:
    - "on some consequences of the permutation paradigm for data anonymization centrality of permutation matrices universal measures of disclosure risk and information loss evaluation by dominance"

Connected through hyperedge HE-4 (induced by source paper "k epsilon anonymity k anonymity with epsilon differential privacy"):
  with members:
    - "optimal differentially private mechanisms for randomised response"
    - "differentially private response mechanisms on categorical data"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-5 (induced by source paper "privacy by design in big data an overview of privacy enhancing technologies in the era of big data analytics"):
  with members:
    - "enigma decentralized computation platform with guaranteed privacy"
    - "new directions in anonymization permutation paradigm verifiability by subjects and intruders transparency to users"
    - "personal data thinking inside the box"
    - "co utility self enforcing protocols without coordination mechanisms"

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)