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: "semantic autoencoder for zero shot learning"
  Abstract: Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g.~attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g.~attribute prediction) or classification. When applied to test data, which in the context of ZSL contains different (unseen) classes without training data, a ZSL model typically suffers from the project domain shift problem. In this work, we present a novel solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the encoder-decoder paradigm, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models. However, the decoder exerts an additional constraint, that is, the projection/code must be able to reconstruct the original visual feature. We show that with this additional reconstruction constraint, the learned projection function from the seen classes is able to generalise better to the new unseen classes. Importantly, the encoder and decoder are linear and symmetric which enable us to develop an extremely efficient learning algorithm. Extensive experiments on six benchmark datasets demonstrate that the proposed SAE outperforms significantly the existing ZSL models with the additional benefit of lower computational cost. Furthermore, when the SAE is applied to supervised clustering problem, it also beats the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "crl class representative learning for image classification"):
  with members:
    - "unifying unsupervised domain adaptation and zero shot visual recognition"
    - "meta transfer networks for zero shot learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards universal representation for unseen action recognition"
    - "complementary attributes a new clue to zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "deep image to video adaptation and fusion networks for action recognition"):
  with members:
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "combined static and motion features for deep networks based activity recognition in videos"
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "global temporal representation based cnns for infrared action recognition"
    - "deep cross media knowledge transfer"
    - "exploiting images for video recognition with hierarchical generative adversarial networks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "from anchor generation to distribution alignment learning a discriminative embedding space for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot kernel learning"
    - "transductive unbiased embedding for zero shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "semantically consistent regularization for zero shot recognition"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "gtnet generative transfer network for zero shot object detection"):
  with members:
    - "out of distribution detection for generalized zero shot action recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "generative dual adversarial network for generalized zero shot learning"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "visual data synthesis via gan for zero shot video classification"
    - "feature generating networks for zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "domain segmentation and adjustment for generalized zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generative dual adversarial network for generalized zero shot learning"
    - "robust classification with convolutional prototype learning"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "deep anomaly detection with outlier exposure"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "preserving semantic relations for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "stacked adversarial network for zero shot sketch based image retrieval"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "generative model for zero shot sketch based image retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "a simple exponential family framework for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "addressing target shift in zero shot learning using grouped adversarial learning"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "detecting and correcting for label shift with black box predictors"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial discriminative domain adaptation"
    - "controllable invariance through adversarial feature learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "minimax filter learning to preserve privacy from inference attacks"
    - "label embedding for image classification"

Connected through hyperedge HE-8 (induced by source paper "image captioning with unseen objects"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "neural baby talk"
    - "zero shot object detection by hybrid region embedding"
    - "polarity loss for zero shot object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "decoupled novel object captioner"
    - "focal loss for dense object detection"

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)