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: "the vqa machine learning how to use existing vision algorithms to answer new questions"
  Abstract: One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image,question,answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. We propose here instead a more general and scalable approach which exploits the fact that very good methods to achieve these operations already exist, and thus do not need to be trained. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.

Connected through hyperedge HE-1 (induced by source paper "learning by abstraction the neural state machine"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "measuring compositionality in representation learning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "answer them all toward universal visual question answering models"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"

Connected through hyperedge HE-2 (induced by source paper "relation aware graph attention network for visual question answering"):
  with members:
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "differential attention for visual question answering"
    - "learning visual knowledge memory networks for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "attend to the difference cross modality person re identification via contrastive correlation"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "vrstc occlusion free video person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "person transfer gan to bridge domain gap for person re identification"

Connected through hyperedge HE-4 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "inverse visual question answering a new benchmark and vqa diagnosis tool"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "ivqa inverse visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "the promise of premise harnessing question premises in visual question answering"
    - "creativity generating diverse questions using variational autoencoders"
    - "active learning for visual question answering an empirical study"

Connected through hyperedge HE-6 (induced by source paper "vqa with no questions answers training"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "visual interpretability for deep learning a survey"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "interpreting cnns via decision trees"
    - "network dissection quantifying interpretability of deep visual representations"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interactively transferring cnn patterns for part localization"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"

Connected through hyperedge HE-8 (induced by source paper "object based reasoning in vqa"):
  with members:
    - "recurrent relational networks for complex relational reasoning"
    - "inferring and executing programs for visual reasoning"
    - "neural collaborative filtering"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "detecting visual relationships with deep relational networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "weakly supervised visual grounding of phrases with linguistic structures"

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.CV(Computer Vision and Pattern Recognition)