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: "comparing bayesian network classifiers"
  Abstract: In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BN-learning algorithm. Experimental results show the obtained classifiers, learned using the CI based algorithms, are competitive with (or superior to) the best known classifiers, based on both Bayesian networks and other formalisms; and that the computational time for learning and using these classifiers is relatively small. Moreover, these results also suggest a way to learn yet more effective classifiers; we demonstrate empirically that this new algorithm does work as expected. Collectively, these results argue that BN classifiers deserve more attention in machine learning and data mining communities.

Connected through hyperedge HE-1 (induced by source paper "a review of visual trackers and analysis of its application to mobile robot"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "context aware deep feature compression for high speed visual tracking"
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "joint representation and truncated inference learning for correlation filter based tracking"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"

Connected through hyperedge HE-2 (induced by source paper "explainability in human agent systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "methods for interpreting and understanding deep neural networks"
    - "textual explanations for self driving vehicles"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "explainable neural computation via stack neural module networks"
    - "providing explanations for recommendations in reciprocal environments"
    - "explainable software analytics"

Connected through hyperedge HE-3 (induced by source paper "measuring information leakage in website fingerprinting attacks and defenses"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "bayes not naive security bounds on website fingerprinting defenses"
    - "the effect of dns on tor s anonymity"
    - "k fingerprinting a robust scalable website fingerprinting technique"
    - "toward an efficient website fingerprinting defense"
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-4 (induced by source paper "on robust trimming of bayesian network classifiers"):
  with members:
    - "value of information lattice exploiting probabilistic independence for effective feature subset acquisition"

Connected through hyperedge HE-5 (induced by source paper "measuring information leakage in website fingerprinting attacks"):
  with members:
    - "efficient estimation of mutual information for strongly dependent variables"

Connected through hyperedge HE-6 (induced by source paper "a logic based approach to generatively defined discriminative modeling"):
  with members:
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "joint and conditional estimation of tagging and parsing models"

Connected through hyperedge HE-7 (induced by source paper "maximum margin bayesian networks"):
  with members:
    - "exponential families for conditional random fields"

Connected through hyperedge HE-8 (induced by source paper "evaluation of the performance of the markov blanket bayesian classifier algorithm"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "induction of selective bayesian classifiers"
    - "learning bayesian networks from incomplete databases"

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.LG(Machine Learning)