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: "approximate privacy foundations and quantification"
  Abstract: Increasing use of computers and networks in business, government, recreation, and almost all aspects of daily life has led to a proliferation of online sensitive data about individuals and organizations. Consequently, concern about the privacy of these data has become a top priority, particularly those data that are created and used in electronic commerce. There have been many formulations of privacy and, unfortunately, many negative results about the feasibility of maintaining privacy of sensitive data in realistic networked environments. We formulate communication-complexity-based definitions, both worst-case and average-case, of a problem's privacy-approximation ratio. We use our definitions to investigate the extent to which approximate privacy is achievable in two standard problems: the second-price Vickrey auction and the millionaires problem of Yao. #R##N#For both the second-price Vickrey auction and the millionaires problem, we show that not only is perfect privacy impossible or infeasibly costly to achieve, but even close approximations of perfect privacy suffer from the same lower bounds. By contrast, we show that, if the values of the parties are drawn uniformly at random from {0,...,2^k-1}, then, for both problems, simple and natural communication protocols have privacy-approximation ratios that are linear in k (i.e., logarithmic in the size of the space of possible inputs). We conjecture that this improved privacy-approximation ratio is achievable for any probability distribution.

Connected through hyperedge HE-1 (induced by source paper "on the computational complexities of three privacy measures for large networks under active attack"):
  with members:
    - "on communication protocols that compute almost privately"
    - "de anonymizing social networks"

Connected through hyperedge HE-2 (induced by source paper "on voting and facility location"):
  with members:
    - "who to trust for truthfully maximizing welfare"
    - "on the power of deterministic mechanisms for facility location games"
    - "strategyproof approximation mechanisms for location on networks"

Connected through hyperedge HE-3 (induced by source paper "lower bounds on information complexity via zero communication protocols and applications"):
  with members:
    - "a strong direct product theorem in terms of the smooth rectangle bound"
    - "information complexity versus corruption and applications to orthogonality and gap hamming"
    - "information equals amortized communication"
    - "a discrepancy lower bound for information complexity"
    - "tight bounds for distributed functional monitoring"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "the space complexity of recognizing well parenthesized expressions in the streaming model the index function revisited"
    - "quantum one way communication is exponentially stronger than classical communication"

Connected through hyperedge HE-4 (induced by source paper "take it or leave it running a survey when privacy comes at a cost"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "conducting truthful surveys cheaply"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "truthful mechanisms for agents that value privacy"
    - "privacy aware mechanism design"
    - "selling privacy at auction"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-5 (induced by source paper "on communication protocols that compute almost privately"):
  with members:
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-6 (induced by source paper "truthful mechanisms for agents that value privacy"):
  with members:
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "the exponential mechanism for social welfare private truthful and nearly optimal"
    - "privacy aware mechanism design"
    - "selling privacy at auction"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-7 (induced by source paper "approximate privacy pars for set problems"):
  with members:
    - "universally utility maximizing privacy mechanisms"

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)