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: "rule extraction algorithm for deep neural networks a review"
  Abstract: Despite the highest classification accuracy in wide varieties of application areas, artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This drawback is the reason why researchers have proposed many rule extraction algorithms to solve the problem. Recently, Deep Neural Network (DNN) is achieving a profound result over the standard neural network for classification and recognition problems. It is a hot machine learning area proven both useful and innovative. This paper has thoroughly reviewed various rule extraction algorithms, considering the classification scheme: decompositional, pedagogical, and eclectics. It also presents the evaluation of these algorithms based on the neural network structure with which the algorithm is intended to work. The main contribution of this review is to show that there is a limited study of rule extraction algorithm from DNN.

Connected through hyperedge HE-1 (induced by source paper "lionforests local interpretation of random forests"):
  with members:
    - "lionets local interpretation of neural networks through penultimate layer decoding"
    - "explainable ai for trees from local explanations to global understanding"
    - "techniques for interpretable machine learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-2 (induced by source paper "learning causal state representations of partially observable environments"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "deep variational reinforcement learning for pomdps"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "deep recurrent q learning for partially observable mdps"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "lionforests local interpretation of random forests through path selection"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "techniques for interpretable machine learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "interpreting tree ensembles with intrees"

Connected through hyperedge HE-4 (induced by source paper "explaining explanations an overview of interpretability of machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "a survey of methods for explaining black box models"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "adversarial examples are not easily detected bypassing ten detection methods"

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)