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: "feed forward networks with attention can solve some long term memory problems"
  Abstract: We propose a simplified model of attention which is applicable to feed-forward neural networks and demonstrate that the resulting model can solve the synthetic "addition" and "multiplication" long-term memory problems for sequence lengths which are both longer and more widely varying than the best published results for these tasks.

Connected through hyperedge HE-1 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "attention based deep multiple instance learning"
    - "deepmvs learning multi view stereopsis"
    - "attention based pyramid aggregation network for visual place recognition"
    - "deep attentional structured representation learning for visual recognition"
    - "deep sets"
    - "deep imbalanced attribute classification using visual attention aggregation"

Connected through hyperedge HE-2 (induced by source paper "deep progressive multi scale attention for acoustic event classification"):
  with members:
    - "sound event detection and time frequency segmentation from weakly labelled data"
    - "learning environmental sounds with multi scale convolutional neural network"
    - "attention based deep multiple instance learning"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "multiple instance deep learning for weakly supervised small footprint audio event detection"
    - "deep cnn framework for audio event recognition using weakly labeled web data"
    - "attention and localization based on a deep convolutional recurrent model for weakly supervised audio tagging"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-3 (induced by source paper "an attention mechanism for musical instrument recognition"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "frame level instrument recognition by timbre and pitch"
    - "multitask learning for frame level instrument recognition"
    - "deep sets"
    - "extended playing techniques the next milestone in musical instrument recognition"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "semantic relatedness based re ranker for text spotting"):
  with members:
    - "universal sentence encoder"
    - "visual re ranking with natural language understanding for text spotting"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "visual attention models for scene text recognition"
    - "multichannel variable size convolution for sentence classification"
    - "a decomposable attention model for natural language inference"
    - "breakingnews article annotation by image and text processing"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-5 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "targeted sentiment classification with attentional encoder network"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "self attentive sequential recommendation"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "atrank an attention based user behavior modeling framework for recommendation"

Connected through hyperedge HE-6 (induced by source paper "dyngraph2seq dynamic graph to sequence interpretable learning for health stage prediction in online health forums"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "scalable global alignment graph kernel using random features from node embedding to graph embedding"
    - "sql to text generation with graph to sequence model"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph r cnn for scene graph generation"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "learning deep generative models of graphs"
    - "graph2seq graph to sequence learning with attention based neural networks"

Connected through hyperedge HE-7 (induced by source paper "a domain generalization perspective on listwise context modeling"):
  with members:
    - "globally optimized mutual influence aware ranking in e commerce search"
    - "cross domain regularization for neural ranking models using adversarial learning"
    - "modeling diverse relevance patterns in ad hoc retrieval"
    - "generalizing across domains via cross gradient training"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"
    - "learning to generalize meta learning for domain generalization"
    - "unified deep supervised domain adaptation and generalization"

Connected through hyperedge HE-8 (induced by source paper "hierarchical attentional hybrid neural networks for document classification"):
  with members:
    - "faster support vector machines"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "bidirectional attention flow for machine comprehension"
    - "very deep convolutional networks for text classification"
    - "enriching word vectors with subword information"
    - "a neural attention model for abstractive sentence summarization"

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)