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: "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
  Abstract: Vehicle-to-vehicle communications can be unreliable as interference causes communication failures. Thereby, the information flow topology for a platoon of Connected Autonomous Vehicles (CAVs) can vary dynamically. This limits existing Cooperative Adaptive Cruise Control (CACC) strategies as most of them assume a fixed information flow topology (IFT). To address this problem, we introduce a CACC design that considers a dynamic information flow topology (CACC-DIFT) for CAV platoons. An adaptive Proportional-Derivative (PD) controller under a two-predecessor-following IFT is proposed to reduce the negative effects when communication failures occur. The PD controller parameters are determined to ensure the string stability of the platoon. Further, the designed controller also factors the performance of individual vehicles. Hence, when communication failure occurs, the system will switch to a certain type of CACC instead of degenerating to adaptive cruise control, which improves the control performance considerably. The effectiveness of the proposed CACC-DIFT is validated through numerical experiments based on NGSIM field data. Results indicate that the proposed CACC-DIFT design outperforms a CACC with a predetermined information flow topology.

Connected through hyperedge HE-1 (induced by source paper "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"):
  with members:
    - "youtube av 50k an annotated corpus for comments in autonomous vehicles"
    - "a methodology for studying 802 11p vanet broadcasting performance with practical vehicle distribution"

Connected through hyperedge HE-2 (induced by source paper "semi supervised text regression with conditional generative adversarial networks"):
  with members:
    - "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"
    - "youtube av 50k an annotated corpus for comments in autonomous vehicles"
    - "attributes in multiple facial images"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "music sequence prediction with mixture hidden markov models"):
  with members:
    - "understanding beauty via deep facial features"
    - "anonymousnet natural face de identification with measurable privacy"
    - "component attention guided face super resolution network cagface"
    - "multiclass information flow propagation control under vehicle to vehicle communication environments"
    - "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"
    - "youtube av 50k an annotated corpus for comments in autonomous vehicles"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "the similarity metric"

Connected through hyperedge HE-4 (induced by source paper "youtube av 50k an annotated corpus for comments in autonomous vehicles"):
  with members:
    - "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"
    - "semi supervised text regression with conditional generative adversarial networks"

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)