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: "a theory of pricing private data"
  Abstract: Personal data has value to both its owner and to institutions who would like to analyze it. Privacy mechanisms protect the owner's data while releasing to analysts noisy versions of aggregate query results. But such strict protections of individual's data have not yet found wide use in practice. Instead, Internet companies, for example, commonly provide free services in return for valuable sensitive information from users, which they exploit and sometimes sell to third parties.   As the awareness of the value of the personal data increases, so has the drive to compensate the end user for her private information. The idea of monetizing private data can improve over the narrower view of hiding private data, since it empowers individuals to control their data through financial means.   In this paper we propose a theoretical framework for assigning prices to noisy query answers, as a function of their accuracy, and for dividing the price amongst data owners who deserve compensation for their loss of privacy. Our framework adopts and extends key principles from both differential privacy and query pricing in data markets. We identify essential properties of the price function and micro-payments, and characterize valid solutions.

Connected through hyperedge HE-1 (induced by source paper "optimal pricing of internet of things a machine learning approach"):
  with members:
    - "hybrid pricing for mobile collaborative internet access"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "mobile big data analytics using deep learning and apache spark"
    - "context aware computing for the internet of things a survey"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "a survey of smart data pricing past proposals current plans and future trends"

Connected through hyperedge HE-2 (induced by source paper "data market platforms trading data assets to solve data problems vision paper"):
  with members:
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "revenue maximization for query pricing"
    - "a marketplace for data an algorithmic solution"
    - "truth finding on the deep web is the problem solved"
    - "a survey on truth discovery"

Connected through hyperedge HE-3 (induced by source paper "online pricing with reserve price constraint for personal data markets"):
  with members:
    - "introduction to online convex optimization"
    - "contextual search via intrinsic volumes"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "the design of arbitrage free data pricing schemes"
    - "oracle efficient online learning and auction design"
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "multidimensional binary search for contextual decision making"

Connected through hyperedge HE-4 (induced by source paper "efficient task specific data valuation for nearest neighbor algorithms"):
  with members:
    - "data markets to support ai for all pricing valuation and governance"
    - "towards efficient data valuation based on the shapley value"
    - "model based pricing for machine learning in a data marketplace"
    - "databright towards a global exchange for decentralized data ownership and trusted computation"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "quantifying differential privacy under temporal correlations"
    - "how to balance privacy and money through pricing mechanism in personal data market"
    - "private decayed predicate sums on streams"
    - "selling privacy at auction"

Connected through hyperedge HE-6 (induced by source paper "data markets to support ai for all pricing valuation and governance"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "distributed learning of deep neural network over multiple agents"
    - "no peek a survey of private distributed deep learning"

Connected through hyperedge HE-7 (induced by source paper "achieving data truthfulness and privacy preservation in data markets"):
  with members:
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-8 (induced by source paper "trading location data with bounded personalized privacy loss"):
  with members:
    - "selling privacy at auction"

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)