human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "right for the wrong reasons diagnosing syntactic heuristics in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "probing the need for visual context in multimodal machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vatex a large scale high quality multilingual dataset for video and language research")
  Member "compositional questions do not necessitate multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "adversarial regularization for visual question answering strengths shortcomings and side effects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "understanding dataset design choices for multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "unlearn dataset bias in natural language inference by fitting the residual":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial filters of dataset biases")
  Member "misleading failures of partial input baselines":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unshuffling data for improved generalization")
  Member "on adversarial removal of hypothesis only bias in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unlearn dataset bias in natural language inference by fitting the residual")

Question: Please tell me which class the source paper 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)