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: "deepcas a deep reinforcement learning algorithm for control aware scheduling"
  Abstract: We consider networked control systems consisting of multiple independent closed-loop control subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale industrial systems. In many large-scale settings, the size of the communication network is smaller than the size of the system. In consequence, scheduling issues arise. The main contribution of this paper is to develop a deep reinforcement learning-based \emph{control-aware} scheduling (\textsc{DeepCAS}) algorithm to tackle these issues. We use the following (optimal) design strategy: First, we synthesize an optimal controller for each subsystem; next, we design learning algorithm that adapts to the chosen subsystem (plant) and controller. As a consequence of this adaptation, our algorithm finds a schedule that minimizes the \emph{control loss}. We present empirical results to show that \textsc{DeepCAS} finds schedules with better performance than periodic ones. Finally, we illustrate that our algorithm can be used for \emph{scheduling and resource allocation in more general networked control settings than the above-mentioned one}.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for scheduling in large scale networked control systems"):
  with members:
    - "deep reinforcement learning for wireless sensor scheduling in cyber physical systems"
    - "deep reinforcement learning for event triggered control"
    - "wireless network design for control systems a survey"
    - "adam a method for stochastic optimization"
    - "design of state based schedulers for a network of control loops"

Connected through hyperedge HE-2 (induced by source paper "networked control of nonlinear systems under partial observation using continuous deep q learning"):
  with members:
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for wireless sensor scheduling in cyber physical systems"):
  with members:
    - "sensor scheduling in variance based event triggered estimation with packet drops"
    - "adam a method for stochastic optimization"
    - "power control and coding formulation for state estimation with wireless sensors"
    - "playing atari with deep reinforcement learning"

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)