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: "composition attacks and auxiliary information in data privacy"
  Abstract: Privacy is an increasingly important aspect of data publishing. Reasoning about privacy, however, is fraught with pitfalls. One of the most significant is the auxiliary information (also called external knowledge, background knowledge, or side information) that an adversary gleans from other channels such as the web, public records, or domain knowledge. This paper explores how one can reason about privacy in the face of rich, realistic sources of auxiliary information. Specifically, we investigate the effectiveness of current anonymization schemes in preserving privacy when multiple organizations independently release anonymized data about overlapping populations. 1. We investigate composition attacks, in which an adversary uses independent anonymized releases to breach privacy. We explain why recently proposed models of limited auxiliary information fail to capture composition attacks. Our experiments demonstrate that even a simple instance of a composition attack can breach privacy in practice, for a large class of currently proposed techniques. The class includes k-anonymity and several recent variants. 2. On a more positive note, certain randomization-based notions of privacy (such as differential privacy) provably resist composition attacks and, in fact, the use of arbitrary side information. This resistance enables stand-alone design of anonymization schemes, without the need for explicitly keeping track of other releases. We provide a precise formulation of this property, and prove that an important class of relaxations of differential privacy also satisfy the property. This significantly enlarges the class of protocols known to enable modular design.

Connected through hyperedge HE-1 (induced by source paper "local differential privacy for deep learning"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "an energy driven network function virtualization for multi domain software defined networks"
    - "an efficient and scalable privacy preserving algorithm for big data and data streams"
    - "mvg mechanism differential privacy under matrix valued query"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "towards practical differential privacy for sql queries"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "machine learning models that remember too much"

Connected through hyperedge HE-2 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "building confidential and efficient query services in the cloud with rasp data perturbation"
    - "a framework for high accuracy privacy preserving mining"

Connected through hyperedge HE-3 (induced by source paper "differentially private regression and classification with sparse gaussian processes"):
  with members:
    - "differentially private random decision forests using smooth sensitivity"
    - "functional mechanism regression analysis under differential privacy"
    - "differentially private empirical risk minimization"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-4 (induced by source paper "on the information privacy model the group and composition privacy"):
  with members:
    - "information theory of data privacy"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "achieving data utility privacy tradeoff in internet of medical things a machine learning approach"):
  with members:
    - "achieving efficient and secure data acquisition for cloud supported internet of things in smart grid"
    - "privacy preserving and efficient aggregation based on blockchain for power grid communications in smart communities"
    - "differentially private k means clustering"

Connected through hyperedge HE-6 (induced by source paper "decentralized exploration in multi armed bandits"):
  with members:
    - "corrupt bandits for preserving local privacy"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "best arm identification in linear bandits"
    - "concurrent bandits and cognitive radio networks"
    - "distributed exploration in multi armed bandits"
    - "calibrating data to sensitivity in private data analysis"
    - "distributed non stochastic experts"

Connected through hyperedge HE-7 (induced by source paper "efficient data perturbation for privacy preserving and accurate data stream mining"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-8 (induced by source paper "prochlo strong privacy for analytics in the crowd"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "prio private robust and scalable computation of aggregate statistics"
    - "plausible deniability for privacy preserving data synthesis"
    - "towards practical differential privacy for sql queries"
    - "glimmers resolving the privacy trust quagmire"
    - "oblivious stash shuffle"

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)