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: "towards best practice in explaining neural network decisions with lrp"
  Abstract: Within the last decade, neural network based predictors have demonstrated impressive - and at times super-human - capabilities. This performance is often paid for with an intransparent prediction process and thus has sparked numerous contributions in the novel field of explainable artificial intelligence (XAI). In this paper, we focus on a popular and widely used method of XAI, the Layer-wise Relevance Propagation (LRP). Since its initial proposition LRP has evolved as a method, and a best practice for applying the method has tacitly emerged, based on humanly observed evidence. We investigate - and for the first time quantify - the effect of this current best practice on feedforward neural networks in a visual object detection setting. The results verify that the current, layer-dependent approach to LRP applied in recent literature better represents the model's reasoning, and at the same time increases the object localization and class discriminativity of LRP.

Connected through hyperedge HE-1 (induced by source paper "verifying deep learning based decisions for facial expression recognition"):
  with members:
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "innvestigate neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "on the understanding and interpretation of machine learning predictions in clinical gait analysis using explainable artificial intelligence"):
  with members:
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "automatic classification of functional gait disorders"
    - "methods for interpreting and understanding deep neural networks"
    - "explaining the unique nature of individual gait patterns with deep learning"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "when explanations lie why modified bp attribution fails"):
  with members:
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

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)