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 distributed integrity catalog for digital repositories"
  Abstract: Digital repositories, either digital preservation systems or archival systems, periodically check the integrity of stored objects to assure users of their correctness. To do so, prior solutions calculate integrity metadata and require the repository to store it alongside the actual data objects. This integrity metadata is essential for regularly verifying the correctness of the stored data objects. To safeguard and detect damage to this metadata, prior solutions rely on widely visible media, that is unaffiliated third parties, to store and provide back digests of the metadata to verify it is intact. However, they do not address recovery of the integrity metadata in case of damage or attack by an adversary. In essence, they do not preserve this metadata. We introduce IntegrityCatalog, a system that collects all integrity related metadata in a single component, and treats them as first class objects, managing both their integrity and their preservation. We introduce a treap-based persistent authenticated dictionary managing arbitrary length key/value pairs, which we use to store all integrity metadata, accessible simply by object name. Additionally, IntegrityCatalog is a distributed system that includes a network protocol that manages both corruption detection and preservation of this metadata, using administrator-selected network peers with two possible roles. Verifiers store and offer attestations on digests and have minimal storage requirements, while preservers efficiently synchronize a complete copy of the catalog to assist in recovery in case of a detected catalog compromise on the local system. We describe our prototype implementation of IntegrityCatalog, measure its performance empirically, and demonstrate its effectiveness in real-world situations, with worst measured throughput of approximately 1K insertions per second, and 2K verified search operations per second.

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)