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: "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
  Abstract: Although "black box" models such as Artificial Neural Networks, Support Vector Machines, and Ensemble Approaches continue to show superior performance in many disciplines, their adoption in the sensitive disciplines (e.g., finance, healthcare) is questionable due to the lack of interpretability and explainability of the model. In fact, future adoption of "black box" models is difficult because of the recent rule of "right of explanation" by the European Union where a user can ask for an explanation behind an algorithmic decision, and the newly proposed bill by the US government, the "Algorithmic Accountability Act", which would require companies to assess their machine learning systems for bias and discrimination and take corrective measures. Top Bankruptcy Prediction Models are A.I.-based and are in need of better explainability -the extent to which the internal working mechanisms of an AI system can be explained in human terms. Although explainable artificial intelligence is an emerging field of research, infusing domain knowledge for better explainability might be a possible solution. In this work, we demonstrate a way to collect and infuse domain knowledge into a "black box" model for bankruptcy prediction. Our understanding from the experiments reveals that infused domain knowledge makes the output from the black box model more interpretable and explainable.

Connected through hyperedge HE-1 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "predictive analysis on twitter techniques and applications"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"
    - "k bert enabling language representation with knowledge graph"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "ai reasoning systems pac and applied methods"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"

Connected through hyperedge HE-2 (induced by source paper "domain knowledge aided explainable artificial intelligence for intrusion detection and response"):
  with members:
    - "a parallel random forest algorithm for big data in a spark cloud computing environment"
    - "credit default mining using combined machine learning and heuristic approach"
    - "methods for interpreting and understanding deep neural networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "rationalizing neural predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "towards quantification of explainability in explainable artificial intelligence methods"):
  with members:
    - "assessing the local interpretability of machine learning models"
    - "manipulating and measuring model interpretability"
    - "tip typifying the interpretability of procedures"
    - "scalable bayesian rule lists"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy"):
  with members:
    - "credit default mining using combined machine learning and heuristic approach"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "oversampling for imbalanced learning based on k means and smote"
    - "smote synthetic minority over sampling technique"

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.AI(Artificial Intelligence)