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: "gaze embeddings for zero shot image classification"
  Abstract: Zero-shot image classification using auxiliary information, such as attributes describing discriminative object properties, requires time-consuming annotation by domain experts. We instead propose a method that relies on human gaze as auxiliary information, exploiting that even non-expert users have a natural ability to judge class membership. We present a data collection paradigm that involves a discrimination task to increase the information content obtained from gaze data. Our method extracts discriminative descriptors from the data and learns a compatibility function between image and gaze using three novel gaze embeddings: Gaze Histograms (GH), Gaze Features with Grid (GFG) and Gaze Features with Sequence (GFS). We introduce two new gaze-annotated datasets for fine-grained image classification and show that human gaze data is indeed class discriminative, provides a competitive alternative to expert-annotated attributes, and outperforms other baselines for zero-shot image classification.

Connected through hyperedge HE-1 (induced by source paper "attention flow end to end joint attention estimation"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "connecting gaze scene and attention generalized attention estimation via joint modeling of gaze and scene saliency"
    - "teacher s perception in the classroom"
    - "cbam convolutional block attention module"
    - "dilated residual networks"
    - "squeeze and excitation networks"
    - "supervising neural attention models for video captioning by human gaze data"
    - "residual attention network for image classification"

Connected through hyperedge HE-2 (induced by source paper "human vs machine attention in neural networks a comparative study"):
  with members:
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "reciprocal attention fusion for visual question answering"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "fine grained video categorization with redundancy reduction attention"
    - "reverse attention for salient object detection"
    - "learn to pay attention"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-3 (induced by source paper "a self validation network for object level human attention estimation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "scaling egocentric vision the epic kitchens dataset"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "the kinetics human action video dataset"
    - "instance level salient object segmentation"

Connected through hyperedge HE-4 (induced by source paper "doodle to search practical zero shot sketch based image retrieval"):
  with members:
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "learning cross modal deep embeddings for multi object image retrieval using text and sketch"
    - "image to image translation for cross domain disentanglement"
    - "deep metric learning with angular loss"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "a zero shot learning application in deep drawing process using hyper process model"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "feature generating networks for zero shot learning"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"
    - "zero shot learning via semantic similarity embedding"

Connected through hyperedge HE-6 (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-7 (induced by source paper "zero shot learning with sparse attribute propagation"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot kernel learning"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "learning from noisy web data with category level supervision"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "transductive zero shot learning with a self training dictionary approach"

Connected through hyperedge HE-8 (induced by source paper "zero shot sketch image hashing"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "stochastic generative hashing"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "recovering the missing link predicting class attribute associations for unsupervised 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.CV(Computer Vision and Pattern Recognition)