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: "self regulating supply demand systems"
  Abstract: Supply-demand systems in Smart City sectors such as energy, transportation, telecommunication and others, are subject of rev- olutionary technological transformations with implications for the effectiveness of traditional regulatory practices. Can existing regulatory actions capture the new dynamics and opportunities that Internet of Things technologies bring to supply-demand sys- tems? Regulatory decision-making by governmental officers, policy makers or system operators is usually long-term, operationally offline and top-down. Such decisions may turn out to be ineffective or may even come in conflict with automated, online and bottom-up decisions nowadays performed, on behalf of people, by software agents embedded in the physical assets of modern supply-demand systems, e.g. Smart Grids. This paper contributes a generic decentralized self-regulatory framework, which, in contrast to related work, is shaped around standardized concepts and Internet of Things technologies for an easier adoption and applicability. An evaluation methodology, integrated within this framework, is introduced that allows the systematic assessment of optimality and system constraints, resulting in more informative and meaningful comparisons of different self-regulatory set- tings. Evidence using real-world datasets of energy supply-demand systems confirms the effectiveness and applicability of the self-regulatory framework. It is shown that a higher informational diversity in the options, from which agents make local selec- tions, results in a higher system-wide response and savings under different regulatory scenarios. Several strategies with which agents make selections come along with striking measurable trade-offs between response and savings creating a vast potential for radical online adjustments incentivized by utilities, system operators and policy makers.

Connected through hyperedge HE-1 (induced by source paper "holarchic structures for decentralized deep learning a performance analysis"):
  with members:
    - "socio technical smart grid optimization via decentralized charge control of electric vehicles"
    - "evolving deep neural networks"
    - "hdltex hierarchical deep learning for text classification"

Connected through hyperedge HE-2 (induced by source paper "decentralized socio technical services and applications for the internet of things a testbed self integration"):
  with members:
    - "makesense an iot testbed for social research of indoor activities"
    - "socio technical smart grid optimization via decentralized charge control of electric vehicles"

Connected through hyperedge HE-3 (induced by source paper "structural self adaptation for decentralized pervasive intelligence"):
  with members:
    - "holarchic structures for decentralized deep learning a performance analysis"
    - "socio technical smart grid optimization via decentralized charge control of electric vehicles"
    - "bnb adopt an asynchronous branch and bound dcop algorithm"
    - "on the difficulty of training recurrent neural networks"
    - "improving the performance of k means for color quantization"

Connected through hyperedge HE-4 (induced by source paper "energy internet via packetized management enabling technologies and deployment challenges"):
  with members:
    - "event based electricity metering an autonomous method to determine transmission thresholds"
    - "finite blocklength communications in smart grids for dynamic spectrum access and locally licensed scenarios"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "why smart appliances may result in a stupid energy grid"
    - "increasing the throughput of an unlicensed wireless network through retransmissions"
    - "narrowband iot data transmission procedures for massive machine type communications"
    - "multi layer analysis of iot based systems"
    - "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"

Connected through hyperedge HE-5 (induced by source paper "socio technical smart grid optimization via decentralized charge control of electric vehicles"):
  with members:
    - "an ev charging scheduling mechanism to maximize user convenience and cost efficiency"

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.SY(Systems and Control)