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: "building a large scale multimodal knowledge base system for answering visual queries"
  Abstract: The complexity of the visual world creates significant challenges for comprehensive visual understanding. In spite of recent successes in visual recognition, today's vision systems would still struggle to deal with visual queries that require a deeper reasoning. We propose a knowledge base (KB) framework to handle an assortment of visual queries, without the need to train new classifiers for new tasks. Building such a large-scale multimodal KB presents a major challenge of scalability. We cast a large-scale MRF into a KB representation, incorporating visual, textual and structured data, as well as their diverse relations. We introduce a scalable knowledge base construction system that is capable of building a KB with half billion variables and millions of parameters in a few hours. Our system achieves competitive results compared to purpose-built models on standard recognition and retrieval tasks, while exhibiting greater flexibility in answering richer visual queries.

Connected through hyperedge HE-1 (induced by source paper "knowit vqa answering knowledge based questions about videos"):
  with members:
    - "passage re ranking with bert"
    - "ok vqa a visual question answering benchmark requiring external knowledge"
    - "from recognition to cognition visual commonsense reasoning"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tvqa localized compositional video question answering"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "scr graph spatial causal relationships based graph reasoning network for human action prediction"):
  with members:
    - "peeking into the future predicting future person activities and locations in videos"
    - "graph neural networks a review of methods and applications"
    - "situation recognition with graph neural networks"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "mask r cnn"
    - "the more you know using knowledge graphs for image classification"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "learning visual knowledge memory networks for visual question answering"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "cascade r cnn delving into high quality object detection"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "structured attentions for visual question answering"
    - "weakly supervised dense video captioning"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "hadamard product for low rank bilinear pooling"
    - "attentive pooling networks"

Connected through hyperedge HE-4 (induced by source paper "visual affordance and function understanding a survey"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "geometric affordances from a single example via the interaction tensor"
    - "predicting human activities using stochastic grammar"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "deep affordance grounded sensorimotor object recognition"
    - "transfer of tool affordance and manipulation cues with 3d vision data"
    - "adaptive binarization for weakly supervised affordance segmentation"
    - "learning to act properly predicting and explaining affordances from images"

Connected through hyperedge HE-5 (induced by source paper "iterative visual reasoning beyond convolutions"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a simple neural network module for relational reasoning"
    - "scene graph generation by iterative message passing"
    - "fasttext zip compressing text classification models"
    - "spatial memory for context reasoning in object detection"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "semantic understanding of scenes through the ade20k dataset"

Connected through hyperedge HE-6 (induced by source paper "the wisdom of masses majority subjectivity and semantic similarity in the evaluation of vqa"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "an analysis of visual question answering algorithms"
    - "advances in pre training distributed word representations"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "embodied question answering"

Connected through hyperedge HE-7 (induced by source paper "on the flip side identifying counterexamples in visual question answering"):
  with members:
    - "mutan multimodal tucker fusion for visual question answering"
    - "survey of visual question answering datasets and techniques"
    - "visual question answering a survey of methods and datasets"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"
    - "ask me anything free form visual question answering based on knowledge from external sources"
    - "visual7w grounded question answering in images"
    - "vqa visual question answering"
    - "skip thought vectors"

Connected through hyperedge HE-8 (induced by source paper "spatial memory for context reasoning in object detection"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "neural map structured memory for deep reinforcement learning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "cognitive mapping and planning for visual navigation"
    - "end to end instance segmentation and counting with recurrent attention"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)