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: "contextual outlier interpretation"
  Abstract: Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers does not receive much attention. Interpretation is becoming increasingly important to help people trust and evaluate the developed models through providing intrinsic reasons why the certain outliers are chosen. It is difficult, if not impossible, to simply apply feature selection for explaining outliers due to the distinct characteristics of various detection models, complicated structures of data in certain applications, and imbalanced distribution of outliers and normal instances. In addition, the role of contrastive contexts where outliers locate, as well as the relation between outliers and contexts, are usually overlooked in interpretation. To tackle the issues above, in this paper, we propose a novel Contextual Outlier INterpretation (COIN) method to explain the abnormality of existing outliers spotted by detectors. The interpretability for an outlier is achieved from three aspects: outlierness score, attributes that contribute to the abnormality, and contextual description of its neighborhoods. Experimental results on various types of datasets demonstrate the flexibility and effectiveness of the proposed framework compared with existing interpretation approaches.

Connected through hyperedge HE-1 (induced by source paper "copula based anomaly scoring and localization for large scale high dimensional continuous data"):
  with members:
    - "pyod a python toolbox for scalable outlier detection"
    - "network traffic anomaly detection using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "explainable recommendation a survey and new perspectives"):
  with members:
    - "attention is not not explanation"
    - "the fact taming latent factor models for explainability with factorization trees"
    - "learning disentangled representations for recommendation"
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "attention is not explanation"
    - "incorporating interpretability into latent factor models via fast influence analysis"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "mmalfm explainable recommendation by leveraging reviews and images"

Connected through hyperedge HE-3 (induced by source paper "towards explanation of dnn based prediction with guided feature inversion"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "smoothgrad removing noise by adding noise"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "interpretable explanations of black boxes by meaningful perturbation"

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)