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: "signaling in sensor networks for sequential detection"
  Abstract: Sequential detection problems in sensor networks are considered. The true state of nature/true hypothesis is modeled as a binary random variable $H$ with known prior distribution. There are $N$ sensors making noisy observations about the hypothesis; $\mathcal{N} =\{1,2,\ldots,N\}$ denotes the set of sensors. Sensor $i$ can receive messages from a subset $\mathcal{P}^i \subset \mathcal{N}$ of sensors and send a message to a subset $\mathcal{C}^i \subset \mathcal{N}$. Each sensor is faced with a stopping problem. At each time $t$, based on the observations it has taken so far and the messages it may have received, sensor $i$ can decide to stop and communicate a binary decision to the sensors in $\mathcal{C}^i$, or it can continue taking observations and receiving messages. After sensor $i$'s binary decision has been sent, it becomes inactive. Sensors incur operational costs (cost of taking observations, communication costs etc.) while they are active. In addition, the system incurs a terminal cost that depends on the true hypothesis $H$, the sensors' binary decisions and their stopping times. The objective is to determine decision strategies for all sensors to minimize the total expected cost.

Connected through hyperedge HE-1 (induced by source paper "coding schemes for securing cyber physical systems against stealthy data injection attacks"):
  with members:
    - "a moving horizon hybrid stochastic game for secure control of cyber physical systems"

Connected through hyperedge HE-2 (induced by source paper "dynamic games with asymmetric information common information based perfect bayesian equilibria and sequential decomposition"):
  with members:
    - "signaling for decentralized routing in a queueing network"
    - "common information based markov perfect equilibria for linear gaussian games with asymmetric information"
    - "decentralized stochastic control with partial history sharing a common information approach"

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)