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: "learning natural language inference using bidirectional lstm model and inner attention"
  Abstract: In this paper, we proposed a sentence encoding-based model for recognizing text entailment. In our approach, the encoding of sentence is a two-stage process. Firstly, average pooling was used over word-level bidirectional LSTM (biLSTM) to generate a first-stage sentence representation. Secondly, attention mechanism was employed to replace average pooling on the same sentence for better representations. Instead of using target sentence to attend words in source sentence, we utilized the sentence's first-stage representation to attend words appeared in itself, which is called "Inner-Attention" in our paper . Experiments conducted on Stanford Natural Language Inference (SNLI) Corpus has proved the effectiveness of "Inner-Attention" mechanism. With less number of parameters, our model outperformed the existing best sentence encoding-based approach by a large margin.

Connected through hyperedge HE-1 (induced by source paper "mhsan multi head self attention network for visual semantic embedding"):
  with members:
    - "univse robust visual semantic embeddings via structured semantic representations"
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "bam bottleneck attention module"
    - "stacked cross attention for image text matching"
    - "cbam convolutional block attention module"
    - "simple recurrent units for highly parallelizable recurrence"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"

Connected through hyperedge HE-2 (induced by source paper "conditional self attention for query based summarization"):
  with members:
    - "universal transformers"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models"
    - "enhancing sentence embedding with generalized pooling"
    - "reinforced mnemonic reader for machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "don t take the premise for granted mitigating artifacts in natural language inference"):
  with members:
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "on adversarial examples for character level neural machine translation"
    - "annotation artifacts in natural language inference data"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "analysis methods in neural language processing a survey"
    - "adventure adversarial training for textual entailment with knowledge guided examples"

Connected through hyperedge HE-5 (induced by source paper "star transformer"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "sequence labeling a practical approach"
    - "multi task learning over graph structures"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "contextualized non local neural networks for sequence learning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "make up your mind adversarial generation of inconsistent natural language explanations"):
  with members:
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "annotation artifacts in natural language inference data"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "textual explanations for self driving vehicles"
    - "e snli natural language inference with natural language explanations"
    - "adversarially regularising neural nli models to integrate logical background knowledge"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-7 (induced by source paper "temporal self attention network for medical concept embedding"):
  with members:
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "medical concept embedding with time aware attention"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "integrative analysis of patient health records and neuroimages via memory based graph convolutional network"
    - "scalable and accurate deep learning for electronic health records"
    - "reinforced mnemonic reader for machine comprehension"

Connected through hyperedge HE-8 (induced by source paper "understanding roles and entities datasets and models for natural language inference"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "transforming question answering datasets into natural language inference datasets"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "large scale qa srl parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through 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)