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: "lshtc a benchmark for large scale text classification"
  Abstract: LSHTC is a series of challenges which aims to assess the performance of classification systems in large-scale classification in a a large number of classes (up to hundreds of thousands). This paper describes the dataset that have been released along the LSHTC series. The paper details the construction of the datsets and the design of the tracks as well as the evaluation measures that we implemented and a quick overview of the results. All of these datasets are available online and runs may still be submitted on the online server of the challenges.

Connected through hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "not just a matter of semantics the relationship between visual similarity and semantic similarity"
    - "do convolutional neural networks learn class hierarchy"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "the inaturalist species classification and detection dataset"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "extreme regression for dynamic search advertising"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"
    - "fast label embeddings for extremely large output spaces"
    - "a theoretical analysis of ndcg type ranking measures"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "text classification algorithms a survey"):
  with members:
    - "rmdl random multimodel deep learning for classification"
    - "analysis of railway accidents narratives using deep learning"
    - "one shot and few shot learning of word embeddings"
    - "deep contextualized word representations"
    - "an improvement of data classification using random multimodel deep learning rmdl"
    - "gakco a fast gapped k mer string kernel using counting"
    - "learning important features through propagating activation differences"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-4 (induced by source paper "hyperbolic interaction model for hierarchical multi label classification"):
  with members:
    - "hyperbolic attention networks"
    - "combined convolutional and recurrent neural networks for hierarchical classification of images"
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "joint embedding of words and labels for text classification"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-5 (induced by source paper "x bert extreme multi label text classification with using bidirectional encoder representations from transformers"):
  with members:
    - "bonsai diverse and shallow trees for extreme multi label classification"
    - "haxmlnet hierarchical attention network for extreme multi label text classification"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "stochastic negative mining for learning with large output spaces"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "an analysis of hierarchical text classification using word embeddings"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "lstm a search space odyssey"
    - "an empirical study on large scale text classification with skip gram embeddings"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "deep learning in neural networks"
    - "evaluation measures for hierarchical classification a unified view and novel approaches"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (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-8 (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"

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)