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: "multimodal residual learning for visual qa"
  Abstract: Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.

Connected through hyperedge HE-1 (induced by source paper "anatomy aware 3d human pose estimation in videos"):
  with members:
    - "chirality nets for human pose regression"
    - "trajectory space factorization for deep video based 3d human pose estimation"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "generating multiple hypotheses for 3d human pose estimation with mixture density network"
    - "ordinal depth supervision for 3d human pose estimation"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "adaptive offline quintuplet loss for image text matching"):
  with members:
    - "expressing objects just like words recurrent visual embedding for image text matching"
    - "visual semantic reasoning for image text matching"
    - "saliency guided attention network for image sentence matching"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "hal improved text image matching by mitigating visual semantic hubs"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "identity aware textual visual matching with latent co attention"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-3 (induced by source paper "expressing objects just like words recurrent visual embedding for image text matching"):
  with members:
    - "position focused attention network for image text matching"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"

Connected through hyperedge HE-4 (induced by source paper "residual knowledge distillation"):
  with members:
    - "esrgan enhanced super resolution generative adversarial networks"
    - "an embarrassingly simple approach for knowledge distillation"
    - "self supervised knowledge distillation using singular value decomposition"
    - "adversarial network compression"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "n2n learning network to network compression via policy gradient reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "compact trilinear interaction for visual question answering"):
  with members:
    - "question type guided attention in visual question answering"
    - "bilinear attention networks"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "visual question answering as a meta learning task"
    - "mutan multimodal tucker fusion for visual question answering"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "an analysis of visual question answering algorithms"

Connected through hyperedge HE-8 (induced by source paper "question guided modular routing networks for visual question answering"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "modulating early visual processing by language"
    - "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"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "a simple neural network module for relational reasoning"
    - "blockdrop dynamic inference paths in residual networks"

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)