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: "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
  Abstract: In the last years we have witnessed the appearance of a variety of strategies to design optimal location privacy-preserving mechanisms, in terms of maximizing the adversary's expected error with respect to the users' whereabouts. In this work, we take a closer look at the defenses created by these strategies and show that, even though they are indeed optimal in terms of adversary's correctness, not all of them offer the same protection when looking at other dimensions of privacy. To avoid "bad" choices, we argue that the search for optimal mechanisms must be guided by complementary criteria. We provide two example auxiliary metrics that help in this regard: the conditional entropy, that captures an information-theoretic aspect of the problem; and the worst-case quality loss, that ensures that the output of the mechanism always provides a minimum utility to the users. We describe a new mechanism that maximizes the conditional entropy and is optimal in terms of average adversary error, and compare its performance with previously proposed optimal mechanisms using two real datasets. Our empirical results confirm that no mechanism fares well on every privacy criteria simultaneously, making apparent the need for considering multiple privacy dimensions to have a good understanding of the privacy protection a mechanism provides.

Connected through hyperedge HE-1 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples"):
  with members:
    - "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"
    - "differentially private model publishing for deep learning"
    - "misleading authorship attribution of source code using adversarial learning"
    - "evaluating differentially private machine learning in practice"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "exploiting unintended feature leakage in collaborative learning"
    - "tagvisor a privacy advisor for sharing hashtags"

Connected through hyperedge HE-2 (induced by source paper "the long road to computational location privacy a survey"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "location privacy in cognitive radio networks a survey"
    - "adaptive location privacy with alp"
    - "constructing elastic distinguishability metrics for location privacy"
    - "protecting locations with differential privacy under temporal correlations"
    - "time distortion anonymization for the publication of mobility data with high utility"

Connected through hyperedge HE-3 (induced by source paper "generating optimal privacy protection mechanisms via machine learning"):
  with members:
    - "privacy preserving adversarial networks"
    - "differential privacy as a mutual information constraint"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "privacy games optimal user centric data obfuscation"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "geo indistinguishability differential privacy for location based systems"
    - "compositional closure for bayes risk in probabilistic noninterference"
    - "on an improvement over renyi s equivocation bound"

Connected through hyperedge HE-4 (induced by source paper "on the lack of location privacy in crowdsourcing applications"):
  with members:
    - "the long road to computational location privacy a survey"
    - "please forget where i was last summer the privacy risks of public location meta data"
    - "knock knock who s there membership inference on aggregate location data"
    - "identifying products in online cybercrime marketplaces a dataset for fine grained domain adaptation"
    - "walk2friends inferring social links from mobility profiles"
    - "protecting locations with differential privacy under temporal correlations"
    - "a predictive differentially private mechanism for mobility traces"

Connected through hyperedge HE-5 (induced by source paper "f bleau fast black box leakage estimation"):
  with members:
    - "mine mutual information neural estimation"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "bayes not naive security bounds on website fingerprinting defenses"
    - "membership inference attacks against machine learning models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "geo indistinguishability differential privacy for location based systems"
    - "on an improvement over renyi s equivocation bound"

Connected through hyperedge HE-6 (induced by source paper "i send therefore i leak information leakage in low power wide area networks"):
  with members:
    - "selective jamming of lorawan using commodity hardware"
    - "low power wide area network analysis can lora scale"

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"
    - "collecting telemetry data privately"
    - "pufferfish privacy mechanisms for correlated data"
    - "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 "tagvisor a privacy advisor for sharing hashtags"):
  with members:
    - "knock knock who s there membership inference on aggregate location data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "walk2friends inferring social links from mobility profiles"
    - "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"

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)