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: "reciprocal attention fusion for visual question answering"
  Abstract: Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA). Motivated by the observation that questions can relate to both object instances and their parts, we propose a novel attention mechanism that jointly considers reciprocal relationships between the two levels of visual details. The bottom-up attention thus generated is further coalesced with the top-down information to only focus on the scene elements that are most relevant to a given question. Our design hierarchically fuses multi-modal information i.e., language, object- and gird-level features, through an efficient tensor decomposition scheme. The proposed model improves the state-of-the-art single model performances from 67.9% to 68.2% on VQAv1 and from 65.7% to 67.4% on VQAv2, demonstrating a significant boost.

Connected through hyperedge HE-1 (induced by source paper "accuracy vs complexity a trade off in visual question answering models"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "rubi reducing unimodal biases in visual question answering"
    - "deep modular co attention networks for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-2 (induced by source paper "answer them all toward universal visual question answering models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "vqd visual query detection in natural scenes"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "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"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "human vs machine attention in neural networks a comparative study"):
  with members:
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "fine grained video categorization with redundancy reduction attention"
    - "reverse attention for salient object detection"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "question agnostic attention for visual question answering"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "learning to count objects in natural images for visual question answering"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "an analysis of visual question answering algorithms"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "mask r cnn"
    - "training recurrent answering units with joint loss minimization for vqa"

Connected through hyperedge HE-5 (induced by source paper "from known to the unknown transferring knowledge to answer questions about novel visual and semantic concepts"):
  with members:
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual question answering"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "hadamard product for low rank bilinear pooling"
    - "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)