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 connected autonomous vehicles by factoring communication related constraints"
  Abstract: Abstract   Emergent cooperative adaptive cruise control (CACC) strategies being proposed for platoon formation in the connected autonomous vehicle (CAV) context mostly assume idealized fixed information flow topologies (IFTs) for the platoon, implying guaranteed vehicle-to-vehicle (V2V) communications for the IFT assumed. In reality, V2V communications are unreliable due to failures resulting from communication-related constraints such as interference and information congestion. Since CACC strategies entail continuous information broadcasting, communication failures can occur in congested CAV traffic networks, leading to a platoon’s IFT varying dynamically. To explicitly factor IFT dynamics and to leverage it to enhance the performance of CACC strategies, this study proposes the idea of dynamically optimizing the IFT for CACC, labeled the CACC-OIFT strategy. Under CACC-OIFT, the vehicles in the platoon cooperatively determine in real-time which vehicles will dynamically deactivate or activate the “send” functionality of their V2V communication devices to generate IFTs that optimize the platoon performance in terms of string stability under the ambient traffic conditions. The CACC-OIFT consists of an IFT optimization model and an adaptive Proportional-Derivative (PD) controller. Given the adaptive PD controller with a two-predecessor-following scheme, and the ambient traffic conditions and the platoon size just before the start of a time period, the IFT optimization model determines the optimal IFT that maximizes the expected string stability in terms of the energy of speed oscillations. This expectation is because each IFT has specific degeneration scenarios whose probabilities are determined by the communication failure probabilities for that time period based on the ambient traffic conditions. The optimal IFT is deployed for that time period, and the adaptive PD controller continuously determines the car-following behaviors of the vehicles based on the unfolding degeneration scenario for each time instant within that period. The effectiveness of the proposed CACC-OIFT is validated through numerical experiments in NS-3 based on NGSIM field data. The results indicate that the proposed CACC-OIFT can significantly enhance the string stability of platoon control in an unreliable V2V communication context, outperforming CACCs with fixed IFTs or with passive adaptive schemes for IFT dynamics.

Connected through hyperedge HE-1 (induced by source paper "semi supervised text regression with conditional generative adversarial networks"):
  with members:
    - "youtube av 50k an annotated corpus for comments in autonomous vehicles"
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
    - "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-2 (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"
    - "youtube av 50k an annotated corpus for comments in autonomous vehicles"
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "the similarity metric"

Connected through hyperedge HE-3 (induced by source paper "youtube av 50k an annotated corpus for comments in autonomous vehicles"):
  with members:
    - "semi supervised text regression with conditional generative adversarial networks"
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"

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)