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: "multi label zero shot learning via concept embedding"
  Abstract: Zero Shot Learning (ZSL) enables a learning model to classify instances of an unseen class during training. While most research in ZSL focuses on single-label classification, few studies have been done in multi-label ZSL, where an instance is associated with a set of labels simultaneously, due to the difficulty in modeling complex semantics conveyed by a set of labels. In this paper, we propose a novel approach to multi-label ZSL via concept embedding learned from collections of public users' annotations of multimedia. Thanks to concept embedding, multi-label ZSL can be done by efficiently mapping an instance input features onto the concept embedding space in a similar manner used in single-label ZSL. Moreover, our semantic learning model is capable of embedding an out-of-vocabulary label by inferring its meaning from its co-occurring labels. Thus, our approach allows both seen and unseen labels during the concept embedding learning to be used in the aforementioned instance mapping, which makes multi-label ZSL more flexible and suitable for real applications. Experimental results of multi-label ZSL on images and music tracks suggest that our approach outperforms a state-of-the-art multi-label ZSL model and can deal with a scenario involving out-of-vocabulary labels without re-training the semantics learning model.

Connected through hyperedge HE-1 (induced by source paper "zero shot learning for audio based music classification and tagging"):
  with members:
    - "multi label zero shot human action recognition via joint latent embedding"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "representation learning of music using artist labels"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "fma a dataset for music analysis"
    - "end to end localization and ranking for relative attributes"
    - "convolutional recurrent neural networks for music classification"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-2 (induced by source paper "multi label zero shot human action recognition via joint latent embedding"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "alternative semantic representations for zero shot human action recognition"
    - "transductive zero shot learning with adaptive structural embedding"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "zero shot learning the good the bad and the ugly"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "hcp a flexible cnn framework for multi label image classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-3 (induced by source paper "alternative semantic representations for zero shot human action recognition"):
  with members:
    - "zero shot learning the good the bad and the ugly"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "multi cue zero shot learning with strong supervision"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "going deeper with convolutions"
    - "distributed representations of sentences and documents"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "train once test anywhere zero shot learning for text classification"):
  with members:
    - "fasttext zip compressing text classification models"

Connected through hyperedge HE-5 (induced by source paper "zero shot visual recognition via bidirectional latent embedding"):
  with members:
    - "learning deep representations of fine grained visual descriptions"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "predicting visual exemplars of unseen classes for zero shot learning"
    - "latent embeddings for zero shot classification"
    - "learning attributes equals multi source domain generalization"
    - "how to transfer zero shot object recognition via hierarchical transfer of semantic attributes"

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.LG(Machine Learning)