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: "reducing metadata leakage from encrypted files and communication with purbs"
  Abstract: Most encrypted data formats, such as PGP, leak substantial metadata in their plaintext headers, such as format version, encryption schemes used, the number of recipients who can decrypt the data, and even the identities of those recipients. This leakage can pose security and privacy risks, e.g., by revealing the full membership of a group of collaborators from a single encrypted E-mail between two of them, or enabling an eavesdropper to fingerprint the precise encryption software version and configuration the sender used and to facilitate targeted attacks against specific endpoint software weaknesses. We propose to improve security and privacy hygiene by designing future encrypted data formats such that no one without a relevant decryption key learns anything at all from a ciphertext apart from its length - and learns as little as possible even from that. To achieve this goal we present Padded Uniform Random Blobs or PURBs, an encrypted format functionally similar to PGP but strongly minimizing a ciphertext's leakage via metadata or length. A PURB is indistinguishable from a uniform random bit-string to an observer without a decryption key. Legitimate recipients can efficiently decrypt the PURB even when it is encrypted for any number of recipients' public keys and/or passwords, and when those public keys are of different cryptographic schemes. PURBs use a novel padding scheme to reduce potential information leakage via the ciphertext's length $L$ to the asymptotic minimum of $O(log_2(log_2(L)))$ bits, comparable to padding to a power of two, but with much lower padding overhead of at most $12\%$ which decreases further with large payloads.

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)