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: "towards linguistically generalizable nlp systems a workshop and shared task"
  Abstract: This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.

Connected through hyperedge HE-1 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "quoref a reading comprehension dataset with questions requiring coreferential reasoning"
    - "assessing the benchmarking capacity of machine reading comprehension datasets"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "adversarial nli a new benchmark for natural language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "adversarial nli a new benchmark for natural language understanding"):
  with members:
    - "adversarial filters of dataset biases"
    - "hellaswag can a machine really finish your sentence"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "abductive commonsense reasoning"
    - "codah an adversarially authored question answer dataset for common sense"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "multi task deep neural networks for natural language understanding"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"

Connected through hyperedge HE-3 (induced by source paper "probing what different nlp tasks teach machines about function word comprehension"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "on the convergence of adam and beyond"
    - "under the hood using diagnostic classifiers to investigate and improve how language models track agreement information"
    - "the natural language decathlon multitask learning as question answering"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "comprehensive supersense disambiguation of english prepositions and possessives"

Connected through hyperedge HE-4 (induced by source paper "cycle consistency for robust visual question answering"):
  with members:
    - "bilinear attention networks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "the natural language decathlon multitask learning as question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "ivqa inverse visual question answering"

Connected through hyperedge HE-5 (induced by source paper "undersensitivity in neural reading comprehension"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-6 (induced by source paper "adversarial attacks against fact extraction and verification"):
  with members:
    - "team papelo transformer networks at fever"
    - "combining fact extraction and verification with neural semantic matching networks"
    - "the fact extraction and verification fever shared task"
    - "automated fact checking task formulations methods and future directions"
    - "ukp athene multi sentence textual entailment for claim verification"
    - "hypothesis only baselines in natural language inference"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "achieving verified robustness to symbol substitutions via interval bound propagation"):
  with members:
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "training verified learners with learned verifiers"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "deep contextualized word representations"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "rc qed evaluating natural language derivations in multi hop reading comprehension"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "automated fact checking task formulations methods and future directions"
    - "worldtree a corpus of explanation graphs for elementary science questions supporting multi hop inference"
    - "neural models for reasoning over multiple mentions using coreference"
    - "multi hop inference for sentence level textgraphs how challenging is meaningfully combining information for science question answering"
    - "e snli natural language inference with natural language explanations"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"

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)