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: "on decentralized estimation with active queries"
  Abstract: We consider the problem of decentralized 20 questions with noise for multiple players/agents under the minimum entropy criterion in the setting of stochastic search over a parameter space, with application to target localization. We propose decentralized extensions of the active query-based stochastic search strategy that combines elements from the 20 questions approach and social learning. We prove convergence to correct consensus on the value of the parameter. This framework provides a flexible and tractable mathematical model for decentralized parameter estimation systems based on active querying. We illustrate the effectiveness and robustness of the proposed decentralized collaborative 20 questions algorithm for random network topologies with information sharing.

Connected through hyperedge HE-1 (induced by source paper "resolution limits of noisy 20 questions estimation"):
  with members:
    - "unequal error protection querying policies for the noisy 20 questions problem"
    - "improved target acquisition rates with feedback codes"
    - "exponential strong converse for content identification with lossy recovery"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "the dispersion of nearest neighbor decoding for additive non gaussian channels"
    - "second order coding rates for channels with state"
    - "searching with measurement dependent noise"

Connected through hyperedge HE-2 (induced by source paper "active query driven visual search using probabilistic bisection and convolutional neural networks"):
  with members:
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"
    - "distributed probabilistic bisection search using social learning"
    - "you only look once unified real time object detection"
    - "deep learning face attributes in the wild"
    - "collaborative 20 questions for target localization"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "active testing for face detection and localization"

Connected through hyperedge HE-3 (induced by source paper "a unified framework for manifold landmarking"):
  with members:
    - "learning combinatorial optimization algorithms over graphs"
    - "unbiased estimates for linear regression via volume sampling"
    - "diverse landmark sampling from determinantal point processes for scalable manifold learning"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "active learning with statistical models"

Connected through hyperedge HE-4 (induced by source paper "distributed probabilistic bisection search using social learning"):
  with members:
    - "probabilistic group testing under sum observations a parallelizable 2 approximation for entropy loss"
    - "collaborative 20 questions for target localization"
    - "convergence rate analysis of distributed gossip linear parameter estimation fundamental limits and tradeoffs"
    - "active testing for face detection and localization"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-5 (induced by source paper "asynchronous decentralized 20 questions for adaptive search"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "collaborative 20 questions for target localization"
    - "a fast distributed proximal gradient method"
    - "active testing for face detection and localization"

Connected through hyperedge HE-6 (induced by source paper "optimal solutions for adaptive search problems with entropy objectives"):
  with members:
    - "collaborative 20 questions for target localization"
    - "active testing for face detection and localization"

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)