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: "improving semantic embedding consistency by metric learning for zero shot classification"
  Abstract: This paper addresses the task of zero-shot image classification. The key contribution of the proposed approach is to control the semantic embedding of images -- one of the main ingredients of zero-shot learning -- by formulating it as a metric learning problem. The optimized empirical criterion associates two types of sub-task constraints: metric discriminating capacity and accurate attribute prediction. This results in a novel expression of zero-shot learning not requiring the notion of class in the training phase: only pairs of image/attributes, augmented with a consistency indicator, are given as ground truth. At test time, the learned model can predict the consistency of a test image with a given set of attributes , allowing flexible ways to produce recognition inferences. Despite its simplicity, the proposed approach gives state-of-the-art results on four challenging datasets used for zero-shot recognition evaluation.

Connected through hyperedge HE-1 (induced by source paper "zero shot object detection learning to simultaneously recognize and localize novel concepts"):
  with members:
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "learning class prototypes via structure alignment for zero shot recognition"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "learning deep representations of fine grained visual descriptions"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "predicting visual exemplars of unseen classes for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "attribute based synthetic network abs net learning more from pseudo feature representations"):
  with members:
    - "label embedding for image classification"
    - "what is the best way for extracting meaningful attributes from pictures"
    - "latent embeddings for zero shot classification"
    - "tensorflow a system for large scale machine learning"
    - "transductive multi view zero shot learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "evaluation of output embeddings for fine grained image classification"

Connected through hyperedge HE-4 (induced by source paper "a simple exponential family framework for zero shot learning"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "zero shot learning posed as a missing data problem"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "learning joint feature adaptation for zero shot recognition"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-5 (induced by source paper "semantic softmax loss for zero shot learning"):
  with members:
    - "transductive zero shot learning with a self training dictionary approach"
    - "hashing in the zero shot framework with domain adaptation"
    - "semi supervised vocabulary informed learning"
    - "transductive multi view zero shot learning"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot learning via semantic similarity embedding"
    - "ridge regression hubness and zero shot learning"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"

Connected through hyperedge HE-6 (induced by source paper "learning joint feature adaptation for zero shot recognition"):
  with members:
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "fast zero shot image tagging"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "transductive multi view zero shot learning"
    - "zero shot event detection by multimodal distributional semantic embedding of videos"
    - "zero shot learning via semantic similarity embedding"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"

Connected through hyperedge HE-7 (induced by source paper "hard negative mining for metric learning based zero shot classification"):
  with members:
    - "training region based object detectors with online hard example mining"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot learning via semantic similarity embedding"
    - "zero shot learning via joint latent similarity embedding"
    - "zero shot recognition with unreliable attributes"
    - "very deep convolutional networks for large scale image recognition"

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)