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: "shuffle then assemble learning object agnostic visual relationship features"
  Abstract: Due to the fact that it is prohibitively expensive to completely annotate visual relationships, i.e., the (obj1, rel, obj2) triplets, relationship models are inevitably biased to object classes of limited pairwise patterns, leading to poor generalization to rare or unseen object combinations. Therefore, we are interested in learning object-agnostic visual features for more generalizable relationship models. By "agnostic", we mean that the feature is less likely biased to the classes of paired objects. To alleviate the bias, we propose a novel \texttt{Shuffle-Then-Assemble} pre-training strategy. First, we discard all the triplet relationship annotations in an image, leaving two unpaired object domains without obj1-obj2 alignment. Then, our feature learning is to recover possible obj1-obj2 pairs. In particular, we design a cycle of residual transformations between the two domains, to capture shared but not object-specific visual patterns. Extensive experiments on two visual relationship benchmarks show that by using our pre-trained features, naive relationship models can be consistently improved and even outperform other state-of-the-art relationship models. Code has been made available at: \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "graphical contrastive losses for scene graph parsing"):
  with members:
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "mattnet modular attention network for referring expression comprehension"
    - "large scale visual relationship understanding"
    - "an interpretable model for scene graph generation"
    - "query guided regression network with context policy for phrase grounding"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-2 (induced by source paper "unpaired image captioning via scene graph alignments"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "semantic correlation promoted shape variant context for segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "engaging image captioning via personality"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-3 (induced by source paper "visual relationship detection with relative location mining"):
  with members:
    - "linknet relational embedding for scene graph"
    - "context dependent diffusion network for visual relationship detection"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-4 (induced by source paper "on exploring undetermined relationships for visual relationship detection"):
  with members:
    - "efficient training for positive unlabeled learning"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "detecting and recognizing human object interactions"
    - "neural motifs scene graph parsing with global context"
    - "relation networks for object detection"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-5 (induced by source paper "deeply supervised multimodal attentional translation embeddings for visual relationship detection"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "detecting visual relationships using box attention"
    - "large scale visual relationship understanding"
    - "an interpretable model for scene graph generation"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "learn to pay attention"

Connected through hyperedge HE-6 (induced by source paper "auto encoding scene graphs for image captioning"):
  with members:
    - "panoptic segmentation"
    - "neural baby talk"
    - "learning to count objects in natural images for visual question answering"
    - "context aware visual policy network for sequence level image captioning"
    - "learning to compose dynamic tree structures for visual contexts"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scene graph parsing as dependency parsing"

Connected through hyperedge HE-7 (induced by source paper "unpaired image captioning by language pivoting"):
  with members:
    - "zero resource neural machine translation with multi agent communication game"
    - "texygen a benchmarking platform for text generation models"
    - "improved image captioning via policy gradient optimization of spider"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "mat a multimodal attentive translator for image captioning"

Connected through hyperedge HE-8 (induced by source paper "vqa e explaining elaborating and enhancing your answers for visual questions"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "stack captioning coarse to fine learning for image captioning"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "towards transparent ai systems interpreting visual question answering models"

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)