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: "graph rise graph regularized image semantic embedding"
  Abstract: Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this paper, we present Graph-Regularized Image Semantic Embedding (Graph-RISE), a large-scale neural graph learning framework that allows us to train embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including image classification and triplet ranking. We provide case studies to demonstrate that, qualitatively, image retrieval based on Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.

Connected through hyperedge HE-1 (induced by source paper "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "bottom up object detection by grouping extreme and center points"
    - "neural baby talk"
    - "dropblock a regularization method for convolutional networks"
    - "learning to count objects in natural images for visual question answering"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-2 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "quality estimation for image captions based on large scale human evaluations"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "informative image captioning with external sources of information"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-4 (induced by source paper "learning multilingual word embeddings using image text data"):
  with members:
    - "multilingual multi modal embeddings for natural language processing"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "image pivoting for learning multilingual multimodal representations"
    - "word translation without parallel data"
    - "massively multilingual word embeddings"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

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)