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: "understanding impacts of high order loss approximations and features in deep learning interpretation"
  Abstract: Current methods to interpret deep learning models by generating saliency maps generally rely on two key assumptions. First, they use first-order approximations of the loss function neglecting higher-order terms such as the loss curvatures. Second, they evaluate each feature's importance in isolation, ignoring their inter-dependencies. In this work, we study the effect of relaxing these two assumptions. First, by characterizing a closed-form formula for the Hessian matrix of a deep ReLU network, we prove that, for a classification problem with a large number of classes, if an input has a high confidence classification score, the inclusion of the Hessian term has small impacts in the final solution. We prove this result by showing that in this case the Hessian matrix is approximately of rank one and its leading eigenvector is almost parallel to the gradient of the loss function. Our empirical experiments on ImageNet samples are consistent with our theory. This result can have implications in other related problems such as adversarial examples as well. Second, we compute the importance of group-features in deep learning interpretation by introducing a sparsity regularization term. We use the $L_0-L_1$ relaxation technique along with the proximal gradient descent to have an efficient computation of group feature importance scores. Our empirical results indicate that considering group features can improve deep learning interpretation significantly.

Connected through hyperedge HE-1 (induced by source paper "robust saliency maps with decoy enhanced saliency score"):
  with members:
    - "certifiably robust interpretation in deep learning"
    - "interpreting neural networks using flip points"
    - "counterfactual visual explanations"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deeppink reproducible feature selection in deep neural networks"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-2 (induced by source paper "explainable machine learning in deployment"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "certifai counterfactual explanations for robustness transparency interpretability and fairness of artificial intelligence models"
    - "transparency motivations and challenges"
    - "automatic model monitoring for data streams"
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "explaining explanations in ai"
    - "on the in fidelity and sensitivity for explanations"

Connected through hyperedge HE-3 (induced by source paper "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "explaining deep learning models a bayesian non parametric approach"
    - "sentinet detecting physical attacks against deep learning systems"
    - "textbugger generating adversarial text against real world applications"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-4 (induced by source paper "interactive refinement of cross lingual word embeddings"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "are girls neko or sh ojo cross lingual alignment of non isomorphic embeddings with iterative normalization"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "exploiting cross lingual subword similarities in low resource document classification"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "efficient online scalar annotation with bounded support"

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)