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 robust utility learning framework via inverse optimization"
  Abstract: In many smart infrastructure applications flexibility in achieving sustainability goals can be gained by engaging end-users. However, these users often have heterogeneous preferences that are unknown to the decision-maker tasked with improving operational efficiency. Modeling user interaction as a continuous game between non-cooperative players, we propose a robust parametric utility learning framework that employs constrained feasible generalized least squares estimation with heteroskedastic inference. To improve forecasting performance, we extend the robust utility learning scheme by employing bootstrapping with bagging, bumping, and gradient boosting ensemble methods. Moreover, we estimate the noise covariance which provides approximated correlations between players which we leverage to develop a novel correlated utility learning framework. We apply the proposed methods both to a toy example arising from Bertrand-Nash competition between two firms as well as to data from a social game experiment designed to encourage energy efficient behavior amongst smart building occupants. Using occupant voting data for shared resources such as lighting, we simulate the game defined by the estimated utility functions to demonstrate the performance of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "design benchmarking and explainability analysis of a game theoretic framework towards energy efficiency in smart infrastructure"):
  with members:
    - "limiting gaming opportunities on incentive based demand response programs"
    - "a deep learning and gamification approach to energy conservation at nanyang technological university"
    - "network inference via the time varying graphical lasso"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "a novel graphical lasso based approach towards segmentation analysis in energy game theoretic frameworks"):
  with members:
    - "delayed impact of fair machine learning"
    - "adaptive incentive design"
    - "network inference via the time varying graphical lasso"

Connected through hyperedge HE-3 (induced by source paper "a deep learning and gamification approach to energy conservation at nanyang technological university"):
  with members:
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "segmentation analysis in human centric cyber physical systems using graphical lasso"):
  with members:
    - "adaptive incentive design"
    - "a deep learning and gamification approach to energy conservation at nanyang technological university"
    - "network inference via the time varying graphical lasso"

Connected through hyperedge HE-5 (induced by source paper "incentive design in a distributed problem with strategic agents"):
  with members:
    - "faithful implementations of distributed algorithms and control laws"
    - "game theoretic multi agent control and network cost allocation under communication constraints"
    - "distributed multi step power scheduling and cost allocation for cooperative microgrids"

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.GT(Computer Science and Game Theory)