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: "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
  Abstract: Recurrent neural network architectures combining with attention mechanism, or neural attention model, have shown promising performance recently for the tasks including speech recognition, image caption generation, visual question answering and machine translation. In this paper, neural attention model is applied on two sequence classification tasks, dialogue act detection and key term extraction. In the sequence labeling tasks, the model input is a sequence, and the output is the label of the input sequence. The major difficulty of sequence labeling is that when the input sequence is long, it can include many noisy or irrelevant part. If the information in the whole sequence is treated equally, the noisy or irrelevant part may degrade the classification performance. The attention mechanism is helpful for sequence classification task because it is capable of highlighting important part among the entire sequence for the classification task. The experimental results show that with the attention mechanism, discernible improvements were achieved in the sequence labeling task considered here. The roles of the attention mechanism in the tasks are further analyzed and visualized in this paper.

Connected through hyperedge HE-1 (induced by source paper "towards robust image classification using sequential attention models"):
  with members:
    - "natural adversarial examples"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "attention please adversarial defense via attention rectification and preservation"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "feature denoising for improving adversarial robustness"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "adversarial logit pairing"

Connected through hyperedge HE-2 (induced by source paper "towards interpretable reinforcement learning using attention augmented agents"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning visual question answering by bootstrapping hard attention"
    - "compositional attention networks for machine reasoning"
    - "cram clued recurrent attention model"
    - "a spatiotemporal model with visual attention for video classification"
    - "hierarchical attentive recurrent tracking"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "dialogue act classification with context aware self attention"):
  with members:
    - "multi level gated recurrent neural network for dialog act classification"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "a dual attention hierarchical recurrent neural network for dialogue act classification"
    - "improved dynamic memory network for dialogue act classification with adversarial training"
    - "deep contextualized word representations"
    - "dialogue act recognition via crf attentive structured network"
    - "neural based context representation learning for dialog act classification"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised attention networks for fine grained opinion mining and public health"):
  with members:
    - "attention based deep multiple instance learning"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deriving machine attention from human rationales"
    - "structured attention networks"
    - "multiple instance learning a survey of problem characteristics and applications"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"

Connected through hyperedge HE-5 (induced by source paper "area attention"):
  with members:
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "areas of attention for image captioning"
    - "image captioning with semantic attention"
    - "neural machine translation in linear time"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "medical time series classification with hierarchical attention based temporal convolutional networks a case study of myotonic dystrophy diagnosis"):
  with members:
    - "improving clinical predictions through unsupervised time series representation learning"
    - "attention based deep multiple instance learning"
    - "focusing on what is relevant time series learning and understanding using attention"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-7 (induced by source paper "multi level gated recurrent neural network for dialog act classification"):
  with members:
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "deep neural networks ensemble for detecting medication mentions in tweets"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "an unsupervised and customizable misspelling generator for mining noisy health related text sources"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cap 2017 challenge twitter named entity recognition"
    - "an investigation of recurrent neural architectures for drug name recognition"

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.CL(Computation and Language)