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: "power saving transportation mode identification for large scale applications"
  Abstract: Transportation mode detection with personal devices has been investigated for over ten years due to its importance in monitoring ones' activities, understanding human mobility, and assisting traffic management. However, two main limitations are still preventing it from large-scale deployments: high power consumption, and the lack of high-volume and diverse labeled data. In order to reduce power consumption, existing approaches are sampling using fewer sensors and with lower frequency, which however lead to a lower accuracy. A common way to obtain labeled data is recording the ground truth while collecting data, but such method cannot apply to large-scale deployment due to its inefficiency. To address these issues, we adopt a new low-frequency sampling manner with a hierarchical transportation mode identification algorithm and propose an offline data labeling approach with its manual and automatic implementations. Through a real-world large-scale experiment and comparison with related works, our sampling manner and algorithm are proved to consume much less energy while achieving a competitive accuracy around 85%. The new offline data labeling approach is also validated to be efficient and effective in providing ground truth for model training and testing.

Connected through hyperedge HE-1 (induced by source paper "routing games in the wild efficiency equilibration and regret large scale field experiments in singapore"):
  with members:
    - "learning and trust in auction markets"
    - "data driven estimation of origin destination demand and user cost functions for the optimization of transportation networks"
    - "on the price of anarchy of highly congested nonatomic network games"
    - "the price of anarchy in large games"
    - "robust data driven guarantees in auctions"
    - "learning in auctions regret is hard envy is easy"
    - "econometrics for learning agents"
    - "playing the wrong game smoothness bounds for congestion games with behavioral biases"

Connected through hyperedge HE-2 (induced by source paper "how bad is selfish routing in practice"):
  with members:
    - "data driven estimation of origin destination demand and user cost functions for the optimization of transportation networks"
    - "the price of anarchy in large games"
    - "robust data driven guarantees in auctions"
    - "econometrics for learning agents"

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.CY(Computers and Society)