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: "robust video object tracking via bayesian model averaging based feature fusion"
  Abstract: Abstract. We are concerned with tracking an object of interest in a video stream. We propose an algorithm thatisrobustagainstocclusion,thepresenceofconfusingcolors,abruptchangesintheobjectfeaturesandchangesin scale. We develop the algorithm within a Bayesian modeling framework. The state-space model is used forcapturing the temporal correlation in the sequence of frame images by modeling the underlying dynamics of thetracking system. The Bayesian model averaging (BMA) strategy is proposed for fusing multiclue information inthe observations. Any number of object features is allowed to be involved in the proposed framework. Everyfeature represents one source of information to be fused and is associated with an observation model. The stateinference is performed by employing the particle filter methods. In comparison with the related approaches, theBMA-based tracker is shown to have robustness, expressivity, and comprehensibility. © 2016 Society of Photo-OpticalInstrumentation Engineers (SPIE) [DOI: 10.1117/1.OE.55.8.083102]

Connected through hyperedge HE-1 (induced by source paper "sequential online prediction in the presence of outliers and change points an instant temporal structure learning approach"):
  with members:
    - "state space model based trust evaluation over wireless sensor networks an iterative particle filter approach"
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"

Connected through hyperedge HE-2 (induced by source paper "harnessing low fidelity data to accelerate bayesian optimization via posterior regularization"):
  with members:
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

Connected through hyperedge HE-3 (induced by source paper "temporal data fusion at the edge"):
  with members:
    - "sequential online prediction in the presence of outliers and change points an instant temporal structure learning approach"
    - "generalized product of experts for automatic and principled fusion of gaussian process predictions"

Connected through hyperedge HE-4 (induced by source paper "a survey on trust modeling from a bayesian perspective"):
  with members:
    - "state space model based trust evaluation over wireless sensor networks an iterative particle filter approach"
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "survey of trust models in different network domains"

Connected through hyperedge HE-5 (induced by source paper "particle filtering methods for stochastic optimization with application to large scale empirical risk minimization"):
  with members:
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

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.CV(Computer Vision and Pattern Recognition)