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: "distributed estimation and control of algebraic connectivity over random graphs"
  Abstract: In this paper, we propose a distributed algorithm for the estimation and control of the connectivity of ad-hoc networks in the presence of a random topology. First, given a generic random graph, we introduce a novel stochastic power iteration method that allows each node to estimate and track the algebraic connectivity of the underlying expected graph. Using results from stochastic approximation theory, we prove that the proposed method converges almost surely (a.s.) to the desired value of connectivity even in the presence of imperfect communication scenarios. The estimation strategy is then used as a basic tool to adapt the power transmitted by each node of a wireless network, in order to maximize the network connectivity in the presence of realistic medium access control (MAC) protocols or simply to drive the connectivity toward a desired target value. Numerical results corroborate our theoretical findings, thus illustrating the main features of the algorithm and its robustness to fluctuations of the network graph due to the presence of random link failures.

Connected through hyperedge HE-1 (induced by source paper "self healing ground and air connectivity chains"):
  with members:
    - "failure tolerant connectivity maintenance for robot swarms"
    - "decentralized collaborative transport of fabrics using micro uavs"
    - "decentralized connectivity preserving deployment of large scale robot swarms"
    - "distributed cohesive control for robot swarms maintaining good connectivity in the presence of exterior forces"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "decentralized connectivity control in quadcopters a field study of communication performance"):
  with members:
    - "stop think and roll online gain optimization for resilient multi robot topologies"
    - "ros and buzz consensus based behaviors for heterogeneous teams"

Connected through hyperedge HE-3 (induced by source paper "decentralized connectivity preserving deployment of large scale robot swarms"):
  with members:
    - "decentralized simultaneous multi target exploration using a connected network of multiple robots"
    - "distributed cohesive control for robot swarms maintaining good connectivity in the presence of exterior forces"

Connected through hyperedge HE-4 (induced by source paper "graph neural networks for learning robot team coordination"):
  with members:
    - "review of multi agent algorithms for collective behavior a structural taxonomy"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-5 (induced by source paper "output impedance diffusion into lossy power lines"):
  with members:
    - "output agreement in networks with unmatched disturbances and algebraic constraints"
    - "transmission line parameter identification using pmu measurements"

Connected through hyperedge HE-6 (induced by source paper "how to stop consensus algorithms locally"):
  with members:
    - "novel multidimensional models of opinion dynamics in social networks"

Connected through hyperedge HE-7 (induced by source paper "distributed adaptive learning of graph signals"):
  with members:
    - "multitask diffusion adaptation over networks with common latent representations"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "adaptive least mean squares estimation of graph signals"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "subsampling for graph power spectrum estimation"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-8 (induced by source paper "distributed optimal quantization and power allocation for sensor detection via consensus"):
  with members:
    - "distributed detection and estimation in wireless sensor networks"
    - "quantized fusion rules for energy based distributed detection in wireless sensor networks"
    - "optimal power allocation for distributed detection over mimo channels in wireless sensor networks"

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)