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: "shrinkwrap differentially private query processing in private data federations"
  Abstract: A private data federation is a set of autonomous databases that share a unified query interface offering in-situ evaluation of SQL queries over the union of the sensitive data of its members. Owing to privacy concerns, these systems do not have a trusted data collector that can see all their data and their member databases cannot learn about individual records of other engines. Federations currently achieve this goal by evaluating queries obliviously using secure multiparty computation. This hides the intermediate result cardinality of each query operator by exhaustively padding it. With cascades of such operators, this padding accumulates to a blow-up in the output size of each operator and a proportional loss in query performance. Hence, existing private data federations do not scale well to complex SQL queries over large datasets. #R##N#We introduce Shrinkwrap, a private data federation that offers data owners a differentially private view of the data held by others to improve their performance over oblivious query processing. Shrinkwrap uses computational differential privacy to minimize the padding of intermediate query results, achieving up to 35X performance improvement over oblivious query processing. When the query needs differentially private output, Shrinkwrap provides a trade-off between result accuracy and query evaluation performance.

Connected through hyperedge HE-1 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "understanding the sparse vector technique for differential privacy"

Connected through hyperedge HE-2 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "cryptodl deep neural networks over encrypted data"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-3 (induced by source paper "differentially private sql with bounded user contribution"):
  with members:
    - "sok differential privacies"
    - "detecting violations of differential privacy"
    - "plausible deniability for privacy preserving data synthesis"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "differential privacy an estimation theory based method for choosing epsilon"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-4 (induced by source paper "pr epsilon epsilon ch a system for privacy preserving speech transcription"):
  with members:
    - "privacy preserving adversarial representation learning in asr reality or illusion"
    - "the audio auditor participant level membership inference in internet of things voice services"
    - "emotionless privacy preserving speech analysis for voice assistants"
    - "attacking speaker recognition with deep generative models"
    - "voicemask anonymize and sanitize voice input on mobile devices"
    - "tacotron towards end to end speech synthesis"
    - "wavenet a generative model for raw audio"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-5 (induced by source paper "spreech a system for privacy preserving speech transcription"):
  with members:
    - "privacy preserving adversarial representation learning in asr reality or illusion"
    - "emotionless privacy preserving speech analysis for voice assistants"
    - "attacking speaker recognition with deep generative models"
    - "voicemask anonymize and sanitize voice input on mobile devices"
    - "deep speech scaling up end to end speech recognition"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "kloakdb a platform for analyzing sensitive data with k anonymous query processing"):
  with members:
    - "smcql secure querying for federated databases"
    - "inferring fine grained control flow inside sgx enclaves with branch shadowing"
    - "oblivious query processing"
    - "worst case background knowledge for privacy preserving data publishing"

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)