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: "worst case background knowledge for privacy preserving data publishing"
  Abstract: Recent work has shown the necessity of considering an attacker's background knowledge when reasoning about privacy in data publishing. However, in practice, the data publisher does not know what background knowledge the attacker possesses. Thus, it is important to consider the worst-case. In this paper, we initiate a formal study of worst-case background knowledge. We propose a language that can express any background knowledge about the data. We provide a polynomial time algorithm to measure the amount of disclosure of sensitive information in the worst case, given that the attacker has at most a specified number of pieces of information in this language. We also provide a method to efficiently sanitize the data so that the amount of disclosure in the worst case is less than a specified threshold.

Connected through hyperedge HE-1 (induced by source paper "kloakdb a platform for analyzing sensitive data with k anonymous query processing"):
  with members:
    - "shrinkwrap differentially private query processing in private data federations"
    - "smcql secure querying for federated databases"
    - "inferring fine grained control flow inside sgx enclaves with branch shadowing"
    - "oblivious query processing"

Connected through hyperedge HE-2 (induced by source paper "a survey of skyline query processing"):
  with members:
    - "efficient skyline querying with variable user preferences on nominal attributes"

Connected through hyperedge HE-3 (induced by source paper "on the complexity of t closeness anonymization and related problems"):
  with members:
    - "resolving the complexity of some data privacy problems"
    - "parameterized complexity of k anonymity hardness and tractability"
    - "the hardness and approximation algorithms for l diversity"

Connected through hyperedge HE-4 (induced by source paper "simultaneous discrimination prevention and privacy protection in data publishing and mining"):
  with members:
    - "futurict the road towards ethical ict"
    - "fairness through awareness"

Connected through hyperedge HE-5 (induced by source paper "transparent anonymization thwarting adversaries who know the algorithm"):
  with members:
    - "from statistical knowledge bases to degrees of belief"

Connected through hyperedge HE-6 (induced by source paper "privacy in search logs"):
  with members:
    - "a learning theory approach to non interactive database privacy"
    - "anonymizing unstructured data"
    - "what can we learn privately"

Connected through hyperedge HE-7 (induced by source paper "can the utility of anonymized data be used for privacy breaches"):
  with members:
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-8 (induced by source paper "anonymization with worst case distribution based background knowledge"):
  with members:
    - "composition attacks and auxiliary information in data privacy"

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.DB(Databases)