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: "don t take the easy way out ensemble based methods for avoiding known dataset biases"
  Abstract: State-of-the-art models often make use of superficial patterns in the data that do not generalize well to out-of-domain or adversarial settings. For example, textual entailment models often learn that particular key words imply entailment, irrespective of context, and visual question answering models learn to predict prototypical answers, without considering evidence in the image. In this paper, we show that if we have prior knowledge of such biases, we can train a model to be more robust to domain shift. Our method has two stages: we (1) train a naive model that makes predictions exclusively based on dataset biases, and (2) train a robust model as part of an ensemble with the naive one in order to encourage it to focus on other patterns in the data that are more likely to generalize. Experiments on five datasets with out-of-domain test sets show significantly improved robustness in all settings, including a 12 point gain on a changing priors visual question answering dataset and a 9 point gain on an adversarial question answering test set.

Connected through hyperedge HE-1 (induced by source paper "counterfactual samples synthesizing for robust visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "learning by abstraction the neural state machine"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "simple but effective techniques to reduce biases"
    - "rubi reducing unimodal biases in visual question answering"

Connected through hyperedge HE-2 (induced by source paper "unshuffling data for improved generalization"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "quantifying and alleviating the language prior problem in visual question answering"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "simple but effective techniques to reduce biases"
    - "rubi reducing unimodal biases in visual question answering"
    - "on incorporating semantic prior knowlegde in deep learning through embedding space constraints"

Connected through hyperedge HE-3 (induced by source paper "stance detection benchmark how robust is your stance detection"):
  with members:
    - "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"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "sampling bias in deep active classification an empirical study"
    - "probing neural network comprehension of natural language arguments"

Connected through hyperedge HE-4 (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"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "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-5 (induced by source paper "learning de biased representations with biased representations"):
  with members:
    - "similarity of neural network representations revisited"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"
    - "sunny and dark outside improving answer consistency in vqa through entailed question generation"
    - "only time can tell discovering temporal data for temporal modeling"
    - "repair removing representation bias by dataset resampling"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "natural adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "queens are powerful too mitigating gender bias in dialogue generation"):
  with members:
    - "does gender matter towards fairness in dialogue systems"
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "gender bias in contextualized word embeddings"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "a persona based multi turn conversation model in an adversarial learning framework"

Connected through hyperedge HE-7 (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"
    - "rubi reducing unimodal biases in visual question answering"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "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-8 (induced by source paper "robust natural language inference models with example forgetting"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "simple but effective techniques to reduce biases"
    - "multi task deep neural networks for natural language understanding"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"

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)