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: "smart meter privacy with renewable energy and a finite capacity battery"
  Abstract: We address the smart meter (SM) privacy problem by considering the availability of a renewable energy source (RES) and a battery which can be exploited by a consumer to partially hide the consumption pattern from the utility provider (UP). Privacy is measured by the mutual information rate between the consumer's energy consumption and the renewable energy generation process, and the energy received from the grid, where the latter is known by the UP through the SM readings, and the former two are to be kept private. By expressing the information leakage as an additive quantity, we cast the problem as a stochastic control problem, and formulate the corresponding Bellman equations.

Connected through hyperedge HE-1 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning"):
  with members:
    - "the long road to computational location privacy a survey"
    - "privacy cost trade off in a smart meter system with a renewable energy source and a rechargeable battery"
    - "privacy aware location sharing with deep reinforcement learning"
    - "privacy aware smart metering progress and challenges"
    - "matching anonymized and obfuscated time series to users profiles"
    - "achieving perfect location privacy in wireless devices using anonymization"
    - "information theoretic privacy for smart metering systems with a rechargeable battery"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "universal mutual information privacy guarantees for smart meters"):
  with members:
    - "a tunable measure for information leakage"
    - "privacy aware smart metering progress and challenges"
    - "joint privacy cost optimization in smart electricity metering systems"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "smart meter privacy via the trapdoor channel"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "smart meter privacy a utility privacy framework"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "privacy cost trade off in a smart meter system with a renewable energy source and a rechargeable battery"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "smart meter privacy via the trapdoor channel"
    - "privacy protecting energy management unit through model distribution predictive control"
    - "information theoretic privacy for smart metering systems with a rechargeable battery"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "increasing smart meter privacy through energy harvesting and storage devices"

Connected through hyperedge HE-4 (induced by source paper "privacy aware location sharing with deep reinforcement learning"):
  with members:
    - "the long road to computational location privacy a survey"
    - "privacy cost trade off in a smart meter system with a renewable energy source and a rechargeable battery"
    - "technical privacy metrics a systematic survey"
    - "information theoretic privacy for smart metering systems with a rechargeable battery"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "smart meter privacy via the trapdoor channel"):
  with members:
    - "smart meter privacy a utility privacy framework"

Connected through hyperedge HE-6 (induced by source paper "privacy protecting energy management unit through model distribution predictive control"):
  with members:
    - "increasing smart meter privacy through energy harvesting and storage devices"

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.IT(Information Theory)