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: "the hardness and approximation algorithms for l diversity"
  Abstract: The existing solutions to privacy preserving publication can be classified into the theoretical and heuristic categories. The former guarantees provably low information loss, whereas the latter incurs gigantic loss in the worst case, but is shown empirically to perform well on many real inputs. While numerous heuristic algorithms have been developed to satisfy advanced privacy principles such as l-diversity, t-closeness, etc., the theoretical category is currently limited to k-anonymity which is the earliest principle known to have severe vulnerability to privacy attacks. Motivated by this, we present the first theoretical study on l-diversity, a popular principle that is widely adopted in the literature. First, we show that optimal l-diverse generalization is NP-hard even when there are only 3 distinct sensitive values in the microdata. Then, an (l*d)-approximation algorithm is developed, where d is the dimensionality of the underlying dataset. This is the first known algorithm with a non-trivial bound on information loss. Extensive experiments with real datasets validate the effectiveness and efficiency of proposed solution.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a tutorial on computing t closeness"
    - "privacy preserving deep learning via weight transmission"
    - "a secure and robust scheme for sharing confidential information in iot systems"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "building scalable decentralized payment systems"
    - "chiron privacy preserving machine learning as a service"

Connected through hyperedge HE-2 (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"
    - "worst case background knowledge for privacy preserving data publishing"

Connected through hyperedge HE-3 (induced by source paper "a review of privacy essentials for confidential mobile data transactions"):
  with members:
    - "utilizing noise addition for data privacy an overview"
    - "utility and privacy of data sources can shannon help conceal and reveal information"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-4 (induced by source paper "clustering with diversity"):
  with members:
    - "correlation clustering with a fixed number of clusters"

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)