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: "granger causal attentive mixtures of experts learning important features with neural networks"
  Abstract: Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying data. Here, we present a new approach to estimating feature importance with neural networks based on the idea of distributing the features of interest among experts in an attentive mixture of experts (AME). AMEs use attentive gating networks trained with a Granger-causal objective to learn to jointly produce accurate predictions as well as estimates of feature importance in a single model. Our experiments show (i) that the feature importance estimates provided by AMEs compare favourably to those provided by state-of-the-art methods, (ii) that AMEs are significantly faster at estimating feature importance than existing methods, and (iii) that the associations discovered by AMEs are consistent with those reported by domain experts.

Connected through hyperedge HE-1 (induced by source paper "neural generators of sparse local linear models for achieving both accuracy and interpretability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sg net syntax guided machine reading comprehension"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "a unified approach to interpreting model predictions"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "a deep learning approach to diagnosing multiple sclerosis from smartphone data"):
  with members:
    - "cxplain causal explanations for model interpretation under uncertainty"
    - "not to cry wolf distantly supervised multitask learning in critical care"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "attention is all you need"
    - "a unified approach to interpreting model predictions"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

Connected through hyperedge HE-3 (induced by source paper "retrospective and prospective mixture of generators for task oriented dialogue response generation"):
  with members:
    - "structured fusion networks for dialog"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "a modular task oriented dialogue system using a neural mixture of experts"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "memory augmented dialogue management for task oriented dialogue systems"
    - "multi source domain adaptation with mixture of experts"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-4 (induced by source paper "a modular task oriented dialogue system using a neural mixture of experts"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "multi source domain adaptation with mixture of experts"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "understanding back translation at scale"
    - "key value retrieval networks for task oriented dialogue"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "cxplain causal explanations for model interpretation under uncertainty"):
  with members:
    - "why should you trust my explanation understanding uncertainty in lime explanations"
    - "neural network attributions a causal perspective"
    - "what to expect of classifiers reasoning about logistic regression with missing features"
    - "explaining deep learning models a bayesian non parametric approach"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "self normalizing neural networks"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "very long term field of view prediction for 360 degree video streaming"):
  with members:
    - "predicting head movement in panoramic video a deep reinforcement learning approach"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "effective approaches to attention based neural machine translation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning counterfactual representations for estimating individual dose response curves"):
  with members:
    - "perfect match a simple method for learning representations for counterfactual inference with neural networks"
    - "deep counterfactual networks with propensity dropout"
    - "bayesian inference of individualized treatment effects using multi task gaussian processes"
    - "building machines that learn and think like people"

Connected through hyperedge HE-8 (induced by source paper "variable lag granger causality for time series analysis"):
  with members:
    - "coordination event detection and initiator identification in time series data"
    - "directed information graphs"
    - "on causal and anticausal learning"

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)