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: "adventure adversarial training for textual entailment with knowledge guided examples"
  Abstract: We consider the problem of learning textual entailment models with limited supervision (5K-10K training examples), and present two complementary approaches for it. First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via only a handful of rule templates. Second, to make the entailment model - a discriminator - more robust, we propose the first GAN-style approach for training it using a natural language example generator that iteratively adjusts based on the discriminator's performance. We demonstrate effectiveness using two entailment datasets, where the proposed methods increase accuracy by 4.7% on SciTail and by 2.8% on a 1% training sub-sample of SNLI. Notably, even a single hand-written rule, negate, improves the accuracy on the negation examples in SNLI by 6.1%.

Connected through hyperedge HE-1 (induced by source paper "a cross sentence latent variable model for semi supervised text sequence matching"):
  with members:
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "generating sentences by editing prototypes"
    - "neural natural language inference models enhanced with external knowledge"
    - "deconvolutional latent variable model for text sequence matching"
    - "adversarially regularized autoencoders"

Connected through hyperedge HE-2 (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"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-3 (induced by source paper "knowledge enhanced attention for robust natural language inference"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "improving question answering by commonsense based pre training"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"

Connected through hyperedge HE-4 (induced by source paper "analyzing machine learned representations a natural language case study"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "human few shot learning of compositional instructions"
    - "mutual exclusivity as a challenge for deep neural networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "analyzing compositionality sensitivity of nli models"
    - "analysis methods in neural language processing a survey"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-5 (induced by source paper "linguistic versus latent relations for modeling coherent flow in paragraphs"):
  with members:
    - "describing a knowledge base"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "detecting and explaining causes from text for a time series event"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "variational neural discourse relation recognizer"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-6 (induced by source paper "on adversarial removal of hypothesis only bias in natural language inference"):
  with members:
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "multinomial adversarial networks for multi domain text classification"
    - "annotation artifacts in natural language inference data"
    - "generating natural language adversarial examples"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "adversarially regularising neural nli models to integrate logical background knowledge"

Connected through hyperedge HE-7 (induced by source paper "textual adversarial attack as combinatorial optimization"):
  with members:
    - "modeling semantic compositionality with sememe knowledge"
    - "openhownet an open sememe based lexical knowledge base"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "language modeling with sparse product of sememe experts"
    - "generating natural language adversarial examples"
    - "hotflip white box adversarial examples for text classification"
    - "generating natural adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-8 (induced by source paper "open the boxes of words incorporating sememes into textual adversarial attack"):
  with members:
    - "openhownet an open sememe based lexical knowledge base"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "language modeling with sparse product of sememe experts"
    - "generating natural language adversarial examples"
    - "generating natural adversarial examples"
    - "intriguing properties of neural networks"

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)