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: "separating self expression and visual content in hashtag supervision"
  Abstract: The variety, abundance, and structured nature of hashtags make them an interesting data source for training vision models. For instance, hashtags have the potential to significantly reduce the problem of manual supervision and annotation when learning vision models for a large number of concepts. However, a key challenge when learning from hashtags is that they are inherently subjective because they are provided by users as a form of self-expression. As a consequence, hashtags may have synonyms (different hashtags referring to the same visual content) and may be ambiguous (the same hashtag referring to different visual content). These challenges limit the effectiveness of approaches that simply treat hashtags as image-label pairs. This paper presents an approach that extends upon modeling simple image-label pairs by modeling the joint distribution of images, hashtags, and users. We demonstrate the efficacy of such approaches in image tagging and retrieval experiments, and show how the joint model can be used to perform user-conditional retrieval and tagging.

Connected through hyperedge HE-1 (induced by source paper "clusterfit improving generalization of visual representations"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised pre training of image features on non curated data"
    - "large scale weakly supervised pre training for video action recognition"
    - "revisiting self supervised visual representation learning"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-2 (induced by source paper "deep unified multimodal embeddings for understanding both content and users in social media networks"):
  with members:
    - "integrating text and image determining multimodal document intent in instagram posts"
    - "align2ground weakly supervised phrase grounding guided by image caption alignment"
    - "universal dependency parsing from scratch"
    - "deep learning based recommender system a survey and new perspectives"
    - "exploring the limits of weakly supervised pretraining"
    - "a comprehensive survey of deep learning for image captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attributed social network embedding"

Connected through hyperedge HE-3 (induced by source paper "personalized hashtag recommendation for micro videos"):
  with members:
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial personalized ranking for recommendation"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-4 (induced by source paper "exploring the limits of weakly supervised pretraining"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning transferable architectures for scalable image recognition"
    - "billion scale similarity search with gpus"
    - "squeeze and excitation networks"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "towards accurate multi person pose estimation in the wild"
    - "dual path 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)