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: "what does the crowd say about you evaluating aggregation based location privacy"
  Abstract: Information about people's movements and the locations they visit enables an increasing number of mobility analytics applications, e.g., in the context of urban and transportation planning, In this setting, rather than collecting or sharing raw data, entities often use aggregation as a privacy protection mechanism, aiming to hide individual users' location traces. Furthermore, to bound information leakage from the aggregates, they can perturb the input of the aggregation or its output to ensure that these are differentially private. #R##N#In this paper, we set to evaluate the impact of releasing aggregate location time-series on the privacy of individuals contributing to the aggregation. We introduce a framework allowing us to reason about privacy against an adversary attempting to predict users' locations or recover their mobility patterns. We formalize these attacks as inference problems, and discuss a few strategies to model the adversary's prior knowledge based on the information she may have access to. We then use the framework to quantify the privacy loss stemming from aggregate location data, with and without the protection of differential privacy, using two real-world mobility datasets. We find that aggregates do leak information about individuals' punctual locations and mobility profiles. The density of the observations, as well as timing, play important roles, e.g., regular patterns during peak hours are better protected than sporadic movements. Finally, our evaluation shows that both output and input perturbation offer little additional protection, unless they introduce large amounts of noise ultimately destroying the utility of the data.

Connected through hyperedge HE-1 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "exploiting unintended feature leakage in collaborative learning"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "privacy protective gan for face de identification"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "please forget where i was last summer the privacy risks of public location meta data"):
  with members:
    - "the long road to computational location privacy a survey"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "a survey of location prediction on twitter"
    - "knock knock who s there membership inference on aggregate location data"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "walk2friends inferring social links from mobility profiles"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "explore spatiotemporal and demographic characteristics of human mobility via twitter a case study of chicago"

Connected through hyperedge HE-3 (induced by source paper "protecting spatiotemporal event privacy in continuous location based services"):
  with members:
    - "the long road to computational location privacy a survey"
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "priste from location privacy to spatiotemporal event privacy"
    - "when and where do you want to hide recommendation of location privacy preferences with local differential privacy"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "quantifying differential privacy under temporal correlations"
    - "privacy games optimal user centric data obfuscation"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-4 (induced by source paper "under the hood of membership inference attacks on aggregate location time series"):
  with members:
    - "on the lack of location privacy in crowdsourcing applications"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "differentially private publication of location entropy"
    - "membership inference attacks against machine learning models"
    - "privacy friendly mobility analytics using aggregate location data"
    - "geo spotting mining online location based services for optimal retail store placement"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-5 (induced by source paper "differential privacy for evolving almost periodic datasets with continual linear queries application to energy data privacy"):
  with members:
    - "private continual release of real valued data streams"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "differentially private publication of location entropy"

Connected through hyperedge HE-6 (induced by source paper "priste from location privacy to spatiotemporal event privacy"):
  with members:
    - "protecting locations with differential privacy under temporal correlations"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-7 (induced by source paper "tagvisor a privacy advisor for sharing hashtags"):
  with members:
    - "knock knock who s there membership inference on aggregate location data"
    - "walk2friends inferring social links from mobility profiles"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "fetishizing food in digital age foodporn around the world"
    - "deepcity a feature learning framework for mining location check ins"
    - "quantifying location sociality"
    - "dawn of the selfie era the whos wheres and hows of selfies on instagram"
    - "deep learning for answer sentence selection"

Connected through hyperedge HE-8 (induced by source paper "logan membership inference attacks against generative models"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "generating differentially private datasets using gans"
    - "towards demystifying membership inference attacks"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "understanding membership inferences on well generalized learning models"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "began boundary equilibrium generative adversarial networks"
    - "knock knock who s there membership inference on aggregate location data"

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)