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: "privacy games optimal user centric data obfuscation"
  Abstract: Consider users who share their data (e.g., lo- cation) with an untrusted service provider to obtain a personalized (e.g., location-based) service. Data obfus- cation is a prevalent user-centric approach to protecting users' privacy in such systems: the untrusted entity only receives a noisy version of user's data. Perturbing data before sharing it, however, comes at the price of the users' utility (service quality) experience which is an in- separable design factor of obfuscation mechanisms. The entanglement of the utility loss and the privacy guaran- tee, in addition to the lack of a comprehensive notion of privacy, have led to the design of obfuscation mecha- nisms that are either suboptimal in terms of their utility loss, or ignore the user's information leakage in the past, or are limited to very specific notions of privacy which e.g., do not protect against adaptive inference attacks or the adversary with arbitrary background knowledge. In this paper, we design user-centric obfuscation mech- anisms that impose the minimum utility loss for guar- anteeing user's privacy. We optimize utility subject to a joint guarantee of dierential privacy (indistinguishabil- ity) and distortion privacy (inference error). This dou- ble shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint dierential-disto rtion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the dif- ferential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game be- tween the designer of obfuscation mechanism and the potential adversary, and design adaptive mechanisms that anticipate and protect against optimal inference al- gorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm.

Connected through hyperedge HE-1 (induced by source paper "ensuring privacy in location based services a model based approach"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "geo indistinguishability differential privacy for location based systems"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "misleading authorship attribution of source code using adversarial learning"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "generative adversarial privacy"
    - "poisoning attacks to graph based recommender systems"
    - "graph based security and privacy analytics via collective classification with joint weight learning and propagation"
    - "adversarial binaries for authorship identification"

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"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "quantifying differential privacy under temporal correlations"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-4 (induced by source paper "generating optimal privacy protection mechanisms via machine learning"):
  with members:
    - "privacy preserving adversarial networks"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "differential privacy as a mutual information constraint"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "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-5 (induced by source paper "on the privacy of dk random graphs"):
  with members:
    - "an efficient and robust social network de anonymization attack"
    - "prolonging the hide and seek game optimal trajectory privacy for location based services"
    - "an efficient reconciliation algorithm for social networks"
    - "graph annotations in modeling complex network topologies"
    - "de anonymizing social networks"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-6 (induced by source paper "utility aware and privacy preserving mobile query services"):
  with members:
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "geo indistinguishability differential privacy for location based systems"

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"
    - "pufferfish privacy mechanisms for correlated data"
    - "differential privacy as a mutual information constraint"
    - "heterogeneous differential privacy"

Connected through hyperedge HE-8 (induced by source paper "attriguard a practical defense against attribute inference attacks via adversarial machine learning"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "powerspy location tracking using mobile device power analysis"
    - "calibrating data to sensitivity in private data analysis"

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)