human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "privacy aware time series data sharing with deep reinforcement learning"):
  Abstract: Internet of things (IoT) devices are becoming increasingly popular thanks to many new services and applications they offer. However, in addition to their many benefits, they raise privacy concerns since they share fine-grained time-series user data with untrusted third parties. In this work, we study the privacy-utility trade-off (PUT) in time-series data sharing. Existing approaches to PUT mainly focus on a single data point; however, temporal correlations in time-series data introduce new challenges. Methods that preserve the privacy for the current time may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We consider sharing the distorted version of a user's true data sequence with an untrusted third party. We measure the privacy leakage by the mutual information between the user's true data sequence and shared version. We consider both instantaneous and average distortion between the two sequences, under a given distortion measure, as the utility loss metric. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL). We apply our optimal data release policies to location trace privacy scenario, and evaluate the performance of the proposed policy numerically.

Includes members:
  Member "the long road to computational location privacy a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "please forget where i was last summer the privacy risks of public location meta data")
  Member "privacy cost trade off in a smart meter system with a renewable energy source and a rechargeable battery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy aware location sharing with deep reinforcement learning")
  Member "privacy aware location sharing with deep reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "privacy aware smart metering progress and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning in the air")
  Member "matching anonymized and obfuscated time series to users profiles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asymptotic limits of privacy in bayesian time series matching")
  Member "achieving perfect location privacy in wireless devices using anonymization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotic limits of privacy in bayesian time series matching")
  Member "smart meter privacy with renewable energy and a finite capacity battery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal mutual information privacy guarantees for smart meters")
  Member "information theoretic privacy for smart metering systems with a rechargeable battery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privacy cost trade off in a smart meter system with a renewable energy source and a rechargeable battery")

Question: Please tell me which class the source paper 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.IT(Information Theory)