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: "pufferfish privacy mechanisms for correlated data"
  Abstract: Many modern databases include personal and sensitive correlated data, such as private information on users connected together in a social network, and measurements of physical activity of single subjects across time. However, differential privacy, the current gold standard in data privacy, does not adequately address privacy issues in this kind of data. #R##N#This work looks at a recent generalization of differential privacy, called Pufferfish, that can be used to address privacy in correlated data. The main challenge in applying Pufferfish is a lack of suitable mechanisms. We provide the first mechanism -- the Wasserstein Mechanism -- which applies to any general Pufferfish framework. Since this mechanism may be computationally inefficient, we provide an additional mechanism that applies to some practical cases such as physical activity measurements across time, and is computationally efficient. Our experimental evaluations indicate that this mechanism provides privacy and utility for synthetic as well as real data in two separate domains.

Connected through hyperedge HE-1 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "detecting violations of differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"

Connected through hyperedge HE-2 (induced by source paper "videodp a universal platform for video analytics with differential privacy"):
  with members:
    - "deep cosine metric learning for person re identification"
    - "simple online and realtime tracking with a deep association metric"
    - "quantifying differential privacy under temporal correlations"
    - "mot16 a benchmark for multi object tracking"
    - "faceless person recognition privacy implications in social media"
    - "defeating image obfuscation with deep learning"
    - "fast r cnn"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-3 (induced by source paper "local obfuscation mechanisms for hiding probability distributions"):
  with members:
    - "local distribution obfuscation via probability coupling"
    - "profile based privacy for locally private computations"
    - "generalised differential privacy for text document processing"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "on the compositionality of quantitative information flow"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "differential privacy an economic method for choosing epsilon"

Connected through hyperedge HE-4 (induced by source paper "on privacy of socially contagious attributes"):
  with members:
    - "ties that bind characterizing classes by attributes and social ties"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "multiple infection sources identification with provable guarantees"
    - "identifying infection sources and regions in large networks"
    - "maximizing social influence in nearly optimal time"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-5 (induced by source paper "quantifying differential privacy in continuous data release under temporal correlations"):
  with members:
    - "quantifying differential privacy under temporal correlations"
    - "protecting locations with differential privacy under temporal correlations"
    - "private decayed predicate sums on streams"

Connected through hyperedge HE-6 (induced by source paper "privacy of dependent users against statistical matching"):
  with members:
    - "asymptotic limits of privacy in bayesian time series matching"
    - "a concentration of measure approach to database de anonymization"
    - "single server single message online private information retrieval with side information"
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "fundamental limits of database alignment"
    - "community detection with side information exact recovery under the stochastic block model"
    - "matching graphs with community structure a concentration of measure approach"
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"

Connected through hyperedge HE-7 (induced by source paper "utility optimized local differential privacy mechanisms for distribution estimation"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "one sided differential privacy"
    - "renyi differential privacy"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "collecting telemetry data privately"
    - "differential privacy as a mutual information constraint"
    - "privacy games optimal user centric data obfuscation"
    - "heterogeneous differential privacy"

Connected through hyperedge HE-8 (induced by source paper "relations among different privacy notions"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "differentially private mixture of generative neural networks"
    - "preserving privacy enables coexistence equilibrium of competitive diffusion in social networks"
    - "composition properties of inferential privacy for time series data"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential privacy"

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.LG(Machine Learning)