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: "large scale visual relationship understanding"
  Abstract: Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of   triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved. We learn both a visual and a semantic module that map features from the two modalities into a shared space, where matched pairs of features have to discriminate against those unmatched, but also maintain close distances to semantically similar ones. Benefiting from that, our model can achieve superior performance even when the visual entity categories scale up to more than 80,000, with extremely skewed class distribution. We demonstrate the efficacy of our model on a large and imbalanced benchmark based of Visual Genome that comprises 53,000+ objects and 29,000+ relations, a scale at which no previous work has ever been evaluated at. We show superiority of our model over carefully designed baselines on the original Visual Genome dataset with 80,000+ categories. We also show state-of-the-art performance on the VRD dataset and the scene graph dataset which is a subset of Visual Genome with 200 categories.

Connected through hyperedge HE-1 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"):
  with members:
    - "deep self learning from noisy labels"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "when does label smoothing help"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "dual student breaking the limits of the teacher in semi supervised learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-2 (induced by source paper "structured domain adaptation for unsupervised person re identification"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bidirectional learning for domain adaptation of semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "gid net detecting human object interaction with global and instance dependency"):
  with members:
    - "pose aware multi level feature network for human object interaction detection"
    - "learning human object interactions by graph parsing neural networks"
    - "dual attention network for scene segmentation"
    - "detecting and recognizing human object interactions"
    - "non local neural networks"
    - "learning to detect human object interactions"
    - "attentional pooling for action recognition"
    - "visual translation embedding network for visual relation detection"

Connected through hyperedge HE-4 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-6 (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"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "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"
    - "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-7 (induced by source paper "union visual translation embedding for visual relationship detection and scene graph generation"):
  with members:
    - "neural baby talk"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"

Connected through hyperedge HE-8 (induced by source paper "vrr vg refocusing visually relevant relationships"):
  with members:
    - "graph r cnn for scene graph generation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "exploring visual relationship for image captioning"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"

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)