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: "transductive multi label zero shot learning"
  Abstract: Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.

Connected through hyperedge HE-1 (induced by source paper "representation quality explains adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "dynamic routing between capsules"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-2 (induced by source paper "context aware zero shot learning for object recognition"):
  with members:
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "modular generative adversarial networks"
    - "deep contextualized word representations"
    - "context encoding for semantic segmentation"
    - "semantic autoencoder for zero shot learning"
    - "learning transferable architectures for scalable image recognition"
    - "neural motifs scene graph parsing with global context"

Connected through hyperedge HE-3 (induced by source paper "uncovering why deep neural networks lack robustness representation metrics that link to adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "understanding the one pixel attack propagation maps and locality analysis"
    - "model agnostic dual quality assessment for adversarial machine learning and an analysis of current neural networks and defenses"
    - "adversarial patch"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "dynamic routing between capsules"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"

Connected through hyperedge HE-4 (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"
    - "multi label zero shot learning via concept embedding"
    - "end to end localization and ranking for relative attributes"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-5 (induced by source paper "transductive zero shot hashing for multi label image retrieval"):
  with members:
    - "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"
    - "supervised discrete hashing with relaxation"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "deep hashing network for unsupervised domain adaptation"
    - "hashing in the zero shot framework with domain adaptation"
    - "hashnet deep learning to hash by continuation"
    - "scalable image retrieval by sparse product quantization"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"

Connected through hyperedge HE-6 (induced by source paper "zero shot crowd behavior recognition"):
  with members:
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "transductive multi view zero shot learning"
    - "a robust and efficient video representation for action recognition"
    - "evaluation of output embeddings for fine grained image classification"
    - "semantic embedding space for zero shot action recognition"
    - "deepid net multi stage and deformable deep convolutional neural networks for object detection"
    - "zero shot learning through cross modal transfer"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "classifier and exemplar synthesis for zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "the devil is in the tails fine grained classification in the wild"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-8 (induced by source paper "zero shot object detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "recent advances in zero shot recognition"
    - "visually aligned word embeddings for improving zero shot learning"

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)