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: "yin and yang balancing and answering binary visual questions"
  Abstract: The complex compositional structure of language makes problems at the intersection of vision and language challenging. But language also provides a strong prior that can result in good superficial performance, without the underlying models truly understanding the visual content. This can hinder progress in pushing state of art in the computer vision aspects of multi-modal AI. In this paper, we address binary Visual Question Answering (VQA) on abstract scenes. We formulate this problem as visual verification of concepts inquired in the questions. Specifically, we convert the question to a tuple that concisely summarizes the visual concept to be detected in the image. If the concept can be found in the image, the answer to the question is "yes", and otherwise "no". Abstract scenes play two roles (1) They allow us to focus on the high-level semantics of the VQA task as opposed to the low-level recognition problems, and perhaps more importantly, (2) They provide us the modality to balance the dataset such that language priors are controlled, and the role of vision is essential. In particular, we collect fine-grained pairs of scenes for every question, such that the answer to the question is "yes" for one scene, and "no" for the other for the exact same question. Indeed, language priors alone do not perform better than chance on our balanced dataset. Moreover, our proposed approach matches the performance of a state-of-the-art VQA approach on the unbalanced dataset, and outperforms it on the balanced dataset.

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"
    - "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"

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"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "vqa lol visual question answering under the lens of logic"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "why does a visual question have different answers"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "integrating knowledge and reasoning in image understanding"
    - "deep modular co attention networks for visual question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "a corpus for reasoning about natural language grounded in photographs"

Connected through hyperedge HE-4 (induced by source paper "challenges and prospects in vision and language research"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "vqd visual query detection in natural scenes"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "do explanations make vqa models more predictable to a human"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-5 (induced by source paper "don t take the easy way out ensemble based methods for avoiding known dataset biases"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "probing the need for visual context in multimodal machine translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "understanding dataset design choices for multi hop reasoning"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "misleading failures of partial input baselines"
    - "on adversarial removal of hypothesis only bias in natural language inference"

Connected through hyperedge HE-6 (induced by source paper "multi modality latent interaction network for visual question answering"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "weakly supervised compositional featureaggregation for few shot recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "2nd place solution to the gqa challenge 2019"
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "tallyqa answering complex counting questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-8 (induced by source paper "what makes training multi modal classification networks hard"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "scaling egocentric vision the epic kitchens dataset"
    - "hypothesis only baselines in natural language inference"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "a comparison of five multiple instance learning pooling functions for sound event detection with weak labeling"
    - "slowfast networks for video recognition"

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)