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: "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
  Abstract: Crowdsourcing has been the prevalent paradigm for creating natural language understanding datasets in recent years. A common crowdsourcing practice is to recruit a small number of high-quality workers, and have them massively generate examples. Having only a few workers generate the majority of examples raises concerns about data diversity, especially when workers freely generate sentences. In this paper, we perform a series of experiments showing these concerns are evident in three recent NLP datasets. We show that model performance improves when training with annotator identifiers as features, and that models are able to recognize the most productive annotators. Moreover, we show that often models do not generalize well to examples from annotators that did not contribute to the training set. Our findings suggest that annotator bias should be monitored during dataset creation, and that test set annotators should be disjoint from training set annotators.

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"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "stancy stance classification based on consistency cues"
    - "multi task deep neural networks for natural language understanding"
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "combating adversarial misspellings with robust word recognition"
    - "sampling bias in deep active classification an empirical study"
    - "probing neural network comprehension of natural language arguments"

Connected through hyperedge HE-3 (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"
    - "no training required exploring random encoders for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "simple but effective techniques to reduce biases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "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"
    - "rubi reducing unimodal biases in visual question answering"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "probing neural network comprehension of natural language arguments"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "annotation artifacts in natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "winogrande an adversarial winograd schema challenge at scale"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "solving hard coreference problems"
    - "a hybrid neural network model for commonsense reasoning"
    - "probing neural network comprehension of natural language arguments"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "multinomial adversarial networks for multi domain text classification"

Connected through hyperedge HE-6 (induced by source paper "run through the streets a new dataset and baseline models for realistic urban navigation"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "chalet cornell house agent learning environment"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "unified pragmatic models for generating and following instructions"

Connected through hyperedge HE-7 (induced by source paper "neural conversational qa learning to reason v s exploiting patterns"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "e3 entailment driven extracting and editing for conversational machine reading"
    - "probing neural network comprehension of natural language arguments"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "reasoning over paragraph effects in situations"):
  with members:
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "quarel a dataset and models for answering questions about qualitative relationships"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "race large scale reading comprehension dataset from examinations"

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)