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: "state space model based trust evaluation over wireless sensor networks an iterative particle filter approach"
  Abstract: In this paper we propose a state space modeling approach for trust evaluation in wireless sensor networks. In our state space trust model (SSTM), each sensor node is associated with a trust metric, which measures to what extent the data transmitted from this node would better be trusted by the server node. Given the SSTM, we translate the trust evaluation problem to be a nonlinear state filtering problem. To estimate the state based on the SSTM, a component-wise iterative state inference procedure is proposed to work in tandem with the particle filter, and thus the resulting algorithm is termed as iterative particle filter (IPF). The computational complexity of the IPF algorithm is theoretically linearly related with the dimension of the state. This property is desirable especially for high dimensional trust evaluation and state filtering problems. The performance of the proposed algorithm is evaluated by both simulations and real data analysis.

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:
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "robust video object tracking via bayesian model averaging based feature fusion"
    - "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 "a survey on trust modeling from a bayesian perspective"):
  with members:
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "robust video object tracking via bayesian model averaging based feature fusion"
    - "survey of trust models in different network domains"

Connected through hyperedge HE-3 (induced by source paper "itrace an implicit trust inference method for trust aware collaborative filtering"):
  with members:
    - "online fault tolerant dynamic event region detection in sensor networks via trust model"
    - "empirical analysis of predictive algorithms for collaborative filtering"

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)