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: "smcql secure querying for federated databases"
  Abstract: People and machines are collecting data at an unprecedented rate. Despite this newfound abundance of data, progress has been slow in sharing it for open science, business, and other data-intensive endeavors. Many such efforts are stymied by privacy concerns and regulatory compliance issues. For example, many hospitals are interested in pooling their medical records for research, but none may disclose arbitrary patient records to researchers or other healthcare providers. In this context we propose the Private Data Network (PDN), a federated database for querying over the collective data of mutually distrustful parties. In a PDN, each member database does not reveal its tuples to its peers nor to the query writer. Instead, the user submits a query to an honest broker that plans and coordinates its execution over multiple private databases using secure multiparty computation (SMC). Here, each database's query execution is oblivious, and its program counters and memory traces are agnostic to the inputs of others. We introduce a framework for executing PDN queries named SMCQL. This system translates SQL statements into SMC primitives to compute query results over the union of its source databases without revealing sensitive information about individual tuples to peer data providers or the honest broker. Only the honest broker and the querier receive the results of a PDN query. For fast, secure query evaluation, we explore a heuristics-driven optimizer that minimizes the PDN's use of secure computation and partitions its query evaluation into scalable slices.

Connected through hyperedge HE-1 (induced by source paper "helen maliciously secure coopetitive learning for linear models"):
  with members:
    - "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "spectre attacks exploiting speculative execution"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "prio private robust and scalable computation of aggregate statistics"

Connected through hyperedge HE-2 (induced by source paper "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"):
  with members:
    - "helen maliciously secure coopetitive learning for linear models"
    - "evaluating differentially private machine learning in practice"
    - "hotstuff bft consensus in the lens of blockchain"
    - "dp admm admm based distributed learning with differential privacy"
    - "gazelle a low latency framework for secure neural network inference"
    - "plausible deniability for privacy preserving data synthesis"
    - "differentially private federated learning a client level perspective"
    - "towards practical differential privacy for sql queries"

Connected through hyperedge HE-3 (induced by source paper "conclave secure multi party computation on big data"):
  with members:
    - "towards practical differential privacy for sql queries"
    - "prochlo strong privacy for analytics in the crowd"
    - "sok cryptographically protected database search"
    - "scaling private record linkage using output constrained differential privacy"
    - "quorums quicken queries efficient asynchronous secure multiparty computation"
    - "secure multi party computation in large networks"

Connected through hyperedge HE-4 (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"
    - "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)