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: "enhancing navigation on wikipedia with social tags"
  Abstract: Social tagging has become an interesting approach to improve search and navigation over the actual Web, since it aggregates the tags added by different users to the same resource in a collaborative way. This way, it results in a list of weighted tags describing its resource. Combined to a classical taxonomic classification system such as that by Wikipedia, social tags can enhance document navigation and search. On the one hand, social tags suggest alternative navigation ways, including pivot-browsing, popularity-driven navigation, and filtering. On the other hand, it provides new metadata, sometimes uncovered by documents' content, that can substantially improve document search. In this work, the inclusion of an interface to add user-defined tags describing Wikipedia articles is proposed, as a way to improve article navigation and retrieval. As a result, a prototype on applying tags over Wikipedia is proposed in order to evaluate its effectiveness.

Connected through hyperedge HE-1 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "deep extreme multi label learning"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"

Connected through hyperedge HE-2 (induced by source paper "extreme multi label legal text classification a case study in eu legislation"):
  with members:
    - "attention is not explanation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "explainable prediction of medical codes from clinical text"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "large scale multi label text classification on eu legislation"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "explainable prediction of medical codes from clinical text"
    - "deep contextualized word representations"
    - "obligation and prohibition extraction using hierarchical rnns"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "ranking based autoencoder for extreme multi label classification"):
  with members:
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "on extending neural networks with loss ensembles for text classification"
    - "a structured self attentive sentence embedding"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "training deep neural networks on noisy labels with bootstrapping"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-5 (induced by source paper "contextual memory trees"):
  with members:
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "learning efficient algorithms with hierarchical attentive memory"
    - "prototypical networks for few shot learning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning to remember rare events"
    - "search engine guided non parametric neural machine translation"
    - "one shot learning with memory augmented neural networks"
    - "scaling memory augmented neural networks with sparse reads and writes"

Connected through hyperedge HE-6 (induced by source paper "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "averaging weights leads to wider optima and better generalization"
    - "a structured self attentive sentence embedding"
    - "lshtc a benchmark for large scale text classification"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "sgm sequence generation model for multi label classification"
    - "a no regret generalization of hierarchical softmax to extreme multi label classification"
    - "averaging weights leads to wider optima and better generalization"
    - "bag of tricks for efficient text classification"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "block wise partitioning for extreme multi label classification"):
  with members:
    - "lshtc a benchmark for large scale text classification"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

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.IR(Information Retrieval)