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: "inoculation by fine tuning a method for analyzing challenge datasets"
  Abstract: Several datasets have recently been constructed to expose brittleness in models trained on existing benchmarks. While model performance on these challenge datasets is significantly lower compared to the original benchmark, it is unclear what particular weaknesses they reveal. For example, a challenge dataset may be difficult because it targets phenomena that current models cannot capture, or because it simply exploits blind spots in a model's specific training set. We introduce inoculation by fine-tuning, a new analysis method for studying challenge datasets by exposing models (the metaphorical patient) to a small amount of data from the challenge dataset (a metaphorical pathogen) and assessing how well they can adapt. We apply our method to analyze the NLI "stress tests" (Naik et al., 2018) and the Adversarial SQuAD dataset (Jia and Liang, 2017). We show that after slight exposure, some of these datasets are no longer challenging, while others remain difficult. Our results indicate that failures on challenge datasets may lead to very different conclusions about models, training datasets, and the challenge datasets themselves.

Connected through hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases"):
  with members:
    - "dataset distillation"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "repair removing representation bias by dataset resampling"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"

Connected through hyperedge HE-2 (induced by source paper "what does my qa model know devising controlled probes using expert knowledge"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "overcoming catastrophic forgetting by neuron level plasticity control"
    - "abductive commonsense reasoning"
    - "from f to a on the n y regents science exams an overview of the aristo project"
    - "diversify your datasets analyzing generalization via controlled variance in adversarial datasets"
    - "qasc a dataset for question answering via sentence composition"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "designing and interpreting probes with control tasks"

Connected through hyperedge HE-3 (induced by source paper "unlearn dataset bias in natural language inference by fitting the residual"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "selection bias explorations and debias methods for natural language sentence matching datasets"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "paws paraphrase adversaries from word scrambling"
    - "theoretically principled trade off between robustness and accuracy"
    - "learning robust representations by projecting superficial statistics out"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-4 (induced by source paper "probing natural language inference models through semantic fragments"):
  with members:
    - "can neural networks understand monotonicity reasoning"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "monalog a lightweight system for natural language inference based on monotonicity"
    - "assessing bert s syntactic abilities"
    - "correlating neural and symbolic representations of language"
    - "a logical based corpus for cross lingual evaluation"
    - "posing fair generalization tasks for natural language inference"
    - "investigating bert s knowledge of language five analysis methods with npis"

Connected through hyperedge HE-5 (induced by source paper "a logic driven framework for consistency of neural models"):
  with members:
    - "augmenting neural networks with first order logic"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "be consistent improving procedural text comprehension using label consistency"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "analyzing compositionality sensitivity of nli models"

Connected through hyperedge HE-6 (induced by source paper "mrqa 2019 shared task evaluating generalization in reading comprehension"):
  with members:
    - "an exploration of data augmentation and sampling techniques for domain agnostic question answering"
    - "learning and evaluating general linguistic intelligence"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "the web as a knowledge base for answering complex questions"

Connected through hyperedge HE-7 (induced by source paper "certified robustness to adversarial word substitutions"):
  with members:
    - "combating adversarial misspellings with robust word recognition"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "training verified learners with learned verifiers"
    - "generating natural language adversarial examples"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-8 (induced by source paper "improving generalization by incorporating coverage in natural language inference"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "learning and evaluating general linguistic intelligence"
    - "multi task deep neural networks for natural language understanding"
    - "non entailed subsequences as a challenge for natural language inference"
    - "annotation artifacts in natural language inference data"
    - "the natural language decathlon multitask learning as question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "pair2vec compositional word pair embeddings for cross sentence 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)