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 premise for granted mitigating artifacts in natural language inference"
  Abstract: Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.

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 easy way out ensemble based methods for avoiding known dataset biases"
    - "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 "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 easy way out ensemble based methods for avoiding known dataset biases"
    - "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-3 (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 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"
    - "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"

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)