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: "attentive pooling networks"
  Abstract: In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware of the current input pair, in a way that information from the two input items can directly influence the computation of each other's representations. Along with such representations of the paired inputs, AP jointly learns a similarity measure over projected segments (e.g. trigrams) of the pair, and subsequently, derives the corresponding attention vector for each input to guide the pooling. Our two-way attention mechanism is a general framework independent of the underlying representation learning, and it has been applied to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in our studies. The empirical results, from three very different benchmark tasks of question answering/answer selection, demonstrate that our proposed models outperform a variety of strong baselines and achieve state-of-the-art performance in all the benchmarks.

Connected through hyperedge HE-1 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "tvqa localized compositional video question answering"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "vain attentional multi agent predictive modeling"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "neural relation prediction for simple question answering over knowledge graph"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "improved neural relation detection for knowledge base question answering"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "bilateral multi perspective matching for natural language sentences"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "cfo conditional focused neural question answering with large scale knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis with contextual embeddings and self attention"):
  with members:
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-4 (induced by source paper "review guided helpful answer identification in e commerce"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "joint learning of answer selection and answer summary generation in community question answering"
    - "learning semantic textual similarity from conversations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-5 (induced by source paper "graph neighborhood attentive pooling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diffusion maps for textual network embedding"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-6 (induced by source paper "two headed monster and crossed co attention networks"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "context aware self attention networks"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "scaling neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-7 (induced by source paper "mrnn a multi resolution neural network with duplex attention for document retrieval in the context of question answering"):
  with members:
    - "a multi resolution word embedding for document retrieval from large unstructured knowledge bases"
    - "training a ranking function for open domain question answering"
    - "deep contextualized word representations"
    - "deep relevance ranking using enhanced document query interactions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "text embeddings for retrieval from a large knowledge base"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-8 (induced by source paper "improving textual network embedding with global attention via optimal transport"):
  with members:
    - "improving sequence to sequence learning via optimal transport"
    - "gromov wasserstein alignment of word embedding spaces"
    - "adversarial text generation via feature mover s distance"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "joint embedding of words and labels for text classification"
    - "diffusion maps for textual network embedding"
    - "stochastic answer networks for natural language inference"

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)