human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "active classification for pomdps a kalman like state estimator"):
  Abstract: Author(s): Zois, Daphney-Stavroula; Levorato, Marco; Mitra, Urbashi | Abstract: © 1991-2012 IEEE. The problem of state tracking with active observation control is considered for a system modeled by a discrete-time, finite-state Markov chain observed through conditionally Gaussian measurement vectors. The measurement model statistics are shaped by the underlying state and an exogenous control input, which influence the observations' quality. Exploiting an innovations approach, an approximate minimum mean-squared error (MMSE) filter is derived to estimate the Markov chain system state. To optimize the control strategy, the associated mean-squared error is used as an optimization criterion in a partially observable Markov decision process formulation. A stochastic dynamic programming algorithm is proposed to solve for the optimal solution. To enhance the quality of system state estimates, approximate MMSE smoothing estimators are also derived. Finally, the performance of the proposed framework is illustrated on the problem of physical activity detection in wireless body sensing networks. The power of the proposed framework lies within its ability to accommodate a broad spectrum of active classification applications, including sensor management for object classification and tracking, estimation of sparse signals, and radar scheduling.

Includes members:
  Member "extrinsic jensen shannon divergence applications to variable length coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low complexity algorithms for transmission of short blocks over the bsc with full feedback")
  Member "near optimal adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "active sequential hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity")
  Member "controlled sensing for multihypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "searching for anomalies over composite hypotheses")
  Member "noisy bayesian active learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning in confusion batch active learning with noisy oracle")
  Member "sensor scheduling for energy efficient target tracking in sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "novel sensor scheduling scheme for intruder tracking in energy efficient sensor networks")
  Member "near optimal bayesian active learning with noisy observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "active learning in the overparameterized and interpolating regime")
  Member "algorithms for dynamic spectrum access with learning for cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectrum access in cognitive radio using a two stage reinforcement learning approach")

Question: Please tell me which class the source paper 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)