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: "sensor scheduling in variance based event triggered estimation with packet drops"
  Abstract: This paper considers a remote state estimation problem with multiple sensors observing a dynamical process, where sensors transmit local state estimates over an independent and identically distributed (i.i.d.) packet dropping channel to a remote estimator. At every discrete time instant, the remote estimator decides whether each sensor should transmit or not, with each sensor transmission incurring a fixed energy cost. The channel is shared such that collisions will occur if more than one sensor transmits at a time. Performance is quantified via an optimization problem that minimizes a convex combination of the expected estimation error covariance at the remote estimator and expected energy usage across the sensors. For transmission schedules dependent only on the estimation error covariance at the remote estimator, this work establishes structural results on the optimal scheduling which show that 1) for unstable systems, if the error covariance is large then a sensor will always be scheduled to transmit, and 2) there is a threshold-type behaviour in switching from one sensor transmitting to another. Specializing to the single sensor case, these structural results demonstrate that a threshold policy (i.e. transmit if the error covariance exceeds a certain threshold and don't transmit otherwise) is optimal. We also consider the situation where sensors transmit measurements instead of state estimates, and establish structural results including the optimality of threshold policies for the single sensor, scalar case. These results provide a theoretical justification for the use of such threshold policies in variance based event triggered estimation. Numerical studies confirm the qualitative behaviour predicted by our structural results. An extension of the structural results to Markovian packet drops is also outlined.

Connected through hyperedge HE-1 (induced by source paper "stabilizing scheduling policies for networked control systems"):
  with members:
    - "on stochastic sensor network scheduling for multiple processes"
    - "stabilizing discrete time switched linear systems"

Connected through hyperedge HE-2 (induced by source paper "wireless networked control systems with coding free data transmission for industrial iot"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "mimo over the air computation for high mobility multi modal sensing"
    - "wireless network design for control systems a survey"
    - "state secrecy codes for networked linear systems"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "optimal power allocation for parameter tracking in a distributed amplify and forward sensor network"

Connected through hyperedge HE-3 (induced by source paper "resource aware iot control saving communication through predictive triggering"):
  with members:
    - "feedback control goes wireless guaranteed stability over low power multi hop networks"
    - "distributed event based state estimation for networked systems an lmi approach"
    - "event based state estimation an emulation based approach"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "predictive and self triggering for event based state estimation"
    - "cyber physical control of road freight transport"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-4 (induced by source paper "max min fair sensor scheduling game theoretic perspective and algorithmic solution"):
  with members:
    - "max min fair transmit precoding for multi group multicasting in massive mimo"
    - "attack allocation on remote state estimation in multi systems structural results and asymptotic solution"
    - "an axiomatic theory of fairness in network resource allocation"

Connected through hyperedge HE-5 (induced by source paper "optimal scheduling of multiple sensors over lossy and bandwidth limited channels"):
  with members:
    - "opportunistic scheduling as restless bandits"
    - "attack allocation on remote state estimation in multi systems structural results and asymptotic solution"
    - "infinite horizon optimal transmission power control for remote state estimation over fading channels"
    - "optimal dynamic multicast scheduling for cache enabled content centric wireless networks"
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for wireless sensor scheduling in cyber physical systems"):
  with members:
    - "deepcas a deep reinforcement learning algorithm for control aware scheduling"
    - "adam a method for stochastic optimization"
    - "power control and coding formulation for state estimation with wireless sensors"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "remote state estimation over packet dropping links in the presence of an eavesdropper"):
  with members:
    - "the ceo problem with secrecy constraints"
    - "distributed inference in the presence of eavesdroppers a survey"
    - "filter design with secrecy constraints the mimo gaussian wiretap channel"
    - "directed information causal estimation and communication in continuous time"
    - "mutual information and minimum mean square error in gaussian channels"

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)