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: "trick me if you can human in the loop generation of adversarial examples for question answering"
  Abstract: Adversarial evaluation stress tests a model's understanding of natural language. While past approaches expose superficial patterns, the resulting adversarial examples are limited in complexity and diversity. We propose human-in-the-loop adversarial generation, where human authors are guided to break models. We aid the authors with interpretations of model predictions through an interactive user interface. We apply this generation framework to a question answering task called Quizbowl, where trivia enthusiasts craft adversarial questions. The resulting questions are validated via live human--computer matches: although the questions appear ordinary to humans, they systematically stump neural and information retrieval models. The adversarial questions cover diverse phenomena from multi-hop reasoning to entity type distractors, exposing open challenges in robust question answering.

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 "crowdsourcing the perception of machine teaching"):
  with members:
    - "diversity in machine learning"
    - "the challenge of crafting intelligible intelligence"
    - "meta transfer learning for few shot learning"
    - "an overview of machine teaching"
    - "understanding hand object manipulation by modeling the contextual relationship between actions grasp types and object attributes"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "machine teaching a new paradigm for building machine learning systems"
    - "becoming the expert interactive multi class machine teaching"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial triggers for attacking and analyzing nlp"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "on adversarial examples for character level neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "adversarial patch"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-4 (induced by source paper "misleading failures of partial input baselines"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "blindfold baselines for embodied qa"
    - "interpreting neural networks with nearest neighbors"
    - "visual dialogue without vision or dialogue"

Connected through hyperedge HE-5 (induced by source paper "what question answering can learn from trivia nerds"):
  with members:
    - "mathqa towards interpretable math word problem solving with operation based formalisms"
    - "question answering is a format when is it useful"
    - "quizbowl the case for incremental question answering"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "what makes reading comprehension questions easier"
    - "coqa a conversational question answering challenge"
    - "quac question answering in context"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"

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)