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: "decentralized maximum likelihood estimation for sensor networks composed of nonlinearly coupled dynamical systems"
  Abstract: In this paper, we propose a decentralized sensor network scheme capable to reach a globally optimum maximum-likelihood (ML) estimate through self-synchronization of nonlinearly coupled dynamical systems. Each node of the network is composed of a sensor and a first-order dynamical system initialized with the local measurements. Nearby nodes interact with each other exchanging their state value, and the final estimate is associated to the state derivative of each dynamical system. We derive the conditions on the coupling mechanism guaranteeing that, if the network observes one common phenomenon, each node converges to the globally optimal ML estimate. We prove that the synchronized state is globally asymptotically stable if the coupling strength exceeds a given threshold. Acting on a single parameter, the coupling strength, we show how, in the case of nonlinear coupling, the network behavior can switch from a global consensus system to a spatial clustering system. Finally, we show the effect of the network topology on the scalability properties of the network, and we validate our theoretical findings with simulation results.

Connected through hyperedge HE-1 (induced by source paper "distributed learning from interactions in social networks"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "next in network nonconvex optimization"
    - "exponentially fast parameter estimation in networks using distributed dual averaging"
    - "a distributed classification estimation algorithm for sensor networks"

Connected through hyperedge HE-2 (induced by source paper "distributed detection and estimation in wireless sensor networks"):
  with members:
    - "gossip algorithms for distributed signal processing"
    - "energy scaling laws for distributed inference in random fusion networks"
    - "detection of gauss markov random fields with nearest neighbor dependency"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"
    - "distributed decision through self synchronizing sensor networks in the presence of propagation delays and nonreciprocal channels"

Connected through hyperedge HE-3 (induced by source paper "distributed consensus over wireless sensor networks affected by multipath fading"):
  with members:
    - "distributed decision through self synchronizing sensor networks in the presence of propagation delays and nonreciprocal 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.DC(Distributed, Parallel, and Cluster Computing)