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 protecting energy management unit through model distribution predictive control"
  Abstract: The roll-out of smart meters in electricity networks introduces risks for consumer privacy due to increased measurement frequency and granularity. Through various non-intrusive load monitoring techniques, consumer behavior may be inferred from their metering data. In this paper, we propose an energy management method that reduces energy cost and protects privacy through the minimization of information leakage. The method is based on a model predictive controller that utilizes energy storage and local generation, and that predicts the effects of its actions on the statistics of the actual energy consumption of a consumer and that seen by the grid. Computationally, the method requires solving a mixed-integer quadratic program of manageable size whenever new meter readings are available. We simulate the controller on generated residential load profiles with different prices of privacy loss in a two-tier time-of-use energy pricing environment. Results show that information leakage is effectively reduced at the expense of increased energy cost. The results also show that with the proposed controller the consumer load profile seen by the grid resembles a mixture between that obtained with non-intrusive load leveling and lazy stepping.

Connected through hyperedge HE-1 (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"
    - "smart meter privacy with renewable energy and a finite capacity battery"
    - "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-2 (induced by source paper "privacy aware smart metering progress and challenges"):
  with members:
    - "optimal demand side management for joint privacy cost optimization with energy storage"
    - "smart meter privacy via the trapdoor channel"
    - "information theoretic privacy for smart metering systems with a rechargeable battery"
    - "privacy optimal strategies for smart metering systems with a rechargeable battery"
    - "smart meter privacy with an energy harvesting device and instantaneous power constraints"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "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-3 (induced by source paper "joint privacy cost optimization in smart electricity metering systems"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "optimal demand side management for joint privacy cost optimization with energy storage"
    - "information theoretic privacy for smart metering systems with a rechargeable battery"
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"

Connected through hyperedge HE-4 (induced by source paper "smart meter privacy with renewable energy and a storage device"):
  with members:
    - "sdp relaxation with randomized rounding for energy disaggregation"
    - "privacy optimal strategies for smart metering systems with a rechargeable battery"
    - "smart meter privacy with an energy harvesting device and instantaneous power constraints"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "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)