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: "automatic image annotation via label transfer in the semantic space"
  Abstract: Abstract   Automatic image annotation is among the fundamental problems in computer vision and pattern recognition, and it is becoming increasingly important in order to develop algorithms that are able to search and browse large-scale image collections. In this paper, we propose a label propagation framework based on Kernel Canonical Correlation Analysis (KCCA), which builds a latent semantic space where correlation of visual and textual features are well preserved into a semantic embedding. The proposed approach is robust and can work either when the training set is well annotated by experts, as well as when it is noisy such as in the case of user-generated tags in social media. We report extensive results on four popular datasets. Our results show that our KCCA-based framework can be applied to several state-of-the-art label transfer methods to obtain significant improvements. Our approach works even with the noisy tags of social users, provided that appropriate denoising is performed. Experiments on a large scale setting show that our method can provide some benefits even when the semantic space is estimated on a subset of training images.

Connected through hyperedge HE-1 (induced by source paper "a cnn rnn framework for image annotation from visual cues and social network metadata"):
  with members:
    - "modeling visual context is key to augmenting object detection datasets"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"
    - "webly supervised learning of convolutional networks"
    - "learning structured inference neural networks with label relations"
    - "improving image classification with location context"

Connected through hyperedge HE-2 (induced by source paper "multi label image classification via knowledge distillation from weakly supervised detection"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "kill two birds with one stone weakly supervised neural network for image annotation and tag refinement"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "learning deep latent spaces for multi label classification"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "deep patch learning for weakly supervised object classification and discovery"

Connected through hyperedge HE-3 (induced by source paper "an effective automatic image annotation model via attention model and data equilibrium"):
  with members:
    - "semantic regularisation for recurrent image annotation"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "guiding long short term memory for image caption generation"
    - "deep captioning with multimodal recurrent neural networks m rnn"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "vse ens visual semantic embeddings with efficient negative sampling"):
  with members:
    - "multi instance visual semantic embedding"
    - "social collaborative retrieval"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "on the consistency of auc pairwise optimization"
    - "latent collaborative retrieval"

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)