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 consensus algorithms in sensor networks quantized data and random link failures"
  Abstract: The paper studies the problem of distributed average consensus in sensor networks with quantized data and random link failures. To achieve consensus, dither (small noise) is added to the sensor states before quantization. When the quantizer range is unbounded (countable number of quantizer levels), stochastic approximation shows that consensus is asymptotically achieved with probability one and in mean square to a finite random variable. We show that the meansquared error (m.s.e.) can be made arbitrarily small by tuning the link weight sequence, at a cost of the convergence rate of the algorithm. To study dithered consensus with random links when the range of the quantizer is bounded, we establish uniform boundedness of the sample paths of the unbounded quantizer. This requires characterization of the statistical properties of the supremum taken over the sample paths of the state of the quantizer. This is accomplished by splitting the state vector of the quantizer in two components: one along the consensus subspace and the other along the subspace orthogonal to the consensus subspace. The proofs use maximal inequalities for submartingale and supermartingale sequences. From these, we derive probability bounds on the excursions of the two subsequences, from which probability bounds on the excursions of the quantizer state vector follow. The paper shows how to use these probability bounds to design the quantizer parameters and to explore tradeoffs among the number of quantizer levels, the size of the quantization steps, the desired probability of saturation, and the desired level of accuracy $\epsilon$ away from consensus. Finally, the paper illustrates the quantizer design with a numerical study.

Connected through hyperedge HE-1 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents"):
  with members:
    - "diff dac distributed actor critic for multitask deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "detection under one bit messaging over adaptive networks"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over adaptive networks refined asymptotics and the role of connectivity"
    - "diffusion based adaptive distributed detection steady state performance in the slow adaptation regime"
    - "distributed parameter estimation with quantized communication via running average"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "active sequential hypothesis testing"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "distributed detection over noisy networks large deviations analysis"

Connected through hyperedge HE-3 (induced by source paper "localization in internets of mobile agents a linear approach"):
  with members:
    - "an opportunistic linear convex algorithm for localization in mobile robot networks"
    - "localization in mobile networks via virtual convex hulls"
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"
    - "higher dimensional consensus learning in large scale networks"
    - "fundamental limits of wideband localization part i a general framework"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"

Connected through hyperedge HE-4 (induced by source paper "distributed detection in ad hoc networks through quantized consensus"):
  with members:
    - "quantized consensus by the admm probabilistic versus deterministic quantizers"
    - "distributed detection over noisy networks large deviations analysis"
    - "large deviations performance of consensus innovations distributed detection with non gaussian observations"
    - "topology for distributed inference on graphs"

Connected through hyperedge HE-5 (induced by source paper "source coding optimization for distributed average consensus"):
  with members:
    - "performance limits for noisy multimeasurement vector problems"
    - "an overview of multi processor approximate message passing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "optimal trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "distributed parameter estimation with quantized communication via running average"
    - "on constrained randomized quantization"
    - "toward resource optimal consensus over the wireless medium"

Connected through hyperedge HE-6 (induced by source paper "resilient randomized quantized consensus"):
  with members:
    - "convergence time for unbiased quantized consensus over static and dynamic networks"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "on the strengths of connectivity and robustness in general random intersection graphs"
    - "ergodic randomized algorithms and dynamics over networks"
    - "distributed randomized algorithms for the pagerank computation"
    - "iterative approximate byzantine consensus in arbitrary directed graphs"
    - "convergence time analysis of quantized gossip consensus on digraphs"

Connected through hyperedge HE-7 (induced by source paper "dynamics over signed networks"):
  with members:
    - "structural balance and opinion separation in trust mistrust social networks"
    - "opinion dynamics in social networks with hostile camps consensus vs polarization"
    - "consensus over random graph processes network borel cantelli lemmas for almost sure convergence"
    - "convergence and state reconstruction of time varying multi agent systems from complete observability theory"
    - "the evolution of beliefs over signed social networks"
    - "how agreement and disagreement evolve over random dynamic networks"

Connected through hyperedge HE-8 (induced by source paper "distributed voting ranking with optimal number of states per node"):
  with members:
    - "plurality consensus in the gossip model"
    - "distributed hypothesis testing with social learning and symmetric fusion"
    - "simple dynamics for plurality consensus"

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.MA(Multiagent Systems)