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: "compositional memory for visual question answering"
  Abstract: Visual Question Answering (VQA) emerges as one of the most fascinating topics in computer vision recently. Many state of the art methods naively use holistic visual features with language features into a Long Short-Term Memory (LSTM) module, neglecting the sophisticated interaction between them. This coarse modeling also blocks the possibilities of exploring finer-grained local features that contribute to the question answering dynamically over time. #R##N#This paper addresses this fundamental problem by directly modeling the temporal dynamics between language and all possible local image patches. When traversing the question words sequentially, our end-to-end approach explicitly fuses the features associated to the words and the ones available at multiple local patches in an attention mechanism, and further combines the fused information to generate dynamic messages, which we call episode. We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information. Motivated by recent practices in deep learning, we use auxiliary loss functions during training to improve the performance. Our experiments on two latest public datasets suggest that our method has a superior performance. Notably, on the DARQUAR dataset we advanced the state of the art by 6$\%$, and we also evaluated our approach on the most recent MSCOCO-VQA dataset.

Connected through hyperedge HE-1 (induced by source paper "non monotonic logical reasoning guiding deep learning for explainable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "answering visual what if questions from actions to predicted scene descriptions"
    - "learning conditioned graph structures for interpretable visual question answering"

Connected through hyperedge HE-2 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "what s in a question using visual questions as a form of supervision"):
  with members:
    - "training recurrent answering units with joint loss minimization for vqa"
    - "leveraging visual question answering for image caption ranking"
    - "a focused dynamic attention model for visual question answering"
    - "ambient sound provides supervision for visual learning"
    - "dualnet domain invariant network for visual question answering"
    - "segmentation from natural language expressions"
    - "spice semantic propositional image caption evaluation"
    - "multimodal residual learning for visual qa"

Connected through hyperedge HE-4 (induced by source paper "tips and tricks for visual question answering learnings from the 2017 challenge"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "learning to reason end to end module networks for visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "identity mappings in deep residual networks"
    - "language modeling with gated convolutional networks"
    - "graph structured representations for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "don t just assume look and answer overcoming priors for visual question answering"):
  with members:
    - "an empirical evaluation of visual question answering for novel objects"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "an analysis of visual question answering algorithms"
    - "learning to generalize to new compositions in image understanding"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "analyzing the behavior of visual question answering models"
    - "a focused dynamic attention model for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "an analysis of visual question answering algorithms"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "a focused dynamic attention model for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "multimodal residual learning for visual qa"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "learning to compose neural networks for question answering"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-7 (induced by source paper "c vqa a compositional split of the visual question answering vqa v1 0 dataset"):
  with members:
    - "an empirical evaluation of visual question answering for novel objects"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "learning to generalize to new compositions in image understanding"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "analyzing the behavior of visual question answering models"
    - "a focused dynamic attention model for visual question answering"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-8 (induced by source paper "ask your neurons a deep learning approach to visual question answering"):
  with members:
    - "hadamard product for low rank bilinear pooling"
    - "a focused dynamic attention model for visual question answering"
    - "hierarchical co attention for visual question answering"
    - "dualnet domain invariant network for visual question answering"
    - "segmentation from natural language expressions"
    - "tutorial on answering questions about images with deep learning"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

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)