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: "active transfer learning with zero shot priors reusing past datasets for future tasks"
  Abstract: How can we reuse existing knowledge, in the form of available datasets, when solving a new and apparently unrelated target task from a set of unlabeled data? In this work we make a first contribution to answer this question in the context of image classification. We frame this quest as an active learning problem and use zero-shot classifiers to guide the learning process by linking the new task to the existing classifiers. By revisiting the dual formulation of adaptive SVM, we reveal two basic conditions to choose greedily only the most relevant samples to be annotated. On this basis we propose an effective active learning algorithm which learns the best possible target classification model with minimum human labeling effort. Extensive experiments on two challenging datasets show the value of our approach compared to the state-of-the-art active learning methodologies, as well as its potential to reuse past datasets with minimal effort for future tasks.

Connected through hyperedge HE-1 (induced by source paper "a novel bilevel paradigm for image to image translation"):
  with members:
    - "neural rendering and reenactment of human actor videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "few shot segmentation propagation with guided networks"
    - "every smile is unique landmark guided diverse smile generation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "open set domain adaptation for image and action recognition"):
  with members:
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep hashing network for unsupervised domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "the kinetics human action video dataset"
    - "visda the visual domain adaptation challenge"

Connected through hyperedge HE-3 (induced by source paper "temporal coherence for active learning in videos"):
  with members:
    - "learning loss for active learning"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "unsupervised hard example mining from videos for improved object detection"
    - "active learning for deep object detection"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "the elephant in the room"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-4 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "variational adversarial active learning"
    - "automl a survey of the state of the art"
    - "are all training examples created equal an empirical study"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"

Connected through hyperedge HE-5 (induced by source paper "active transfer learning for persian offline signature verification"):
  with members:
    - "multi representational learning for offline signature verification using multi loss snapshot ensemble of cnns"
    - "learning representations from persian handwriting for offline signature verification a deep transfer learning approach"
    - "offline handwritten signature verification literature review"
    - "signet convolutional siamese network for writer independent offline signature verification"
    - "utsig a persian offline signature dataset"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "deep residual learning for image recognition"

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 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"

Connected through hyperedge HE-8 (induced by source paper "a unified approach for conventional zero shot generalized zero shot and few shot learning"):
  with members:
    - "learning robust visual semantic embeddings"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "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"
    - "fast zero shot image tagging"
    - "label embedding for image classification"

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)