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 cue zero shot learning with strong supervision"
  Abstract: Scaling up visual category recognition to large numbers of classes remains challenging. A promising research direction is zero-shot learning, which does not require any training data to recognize new classes, but rather relies on some form of auxiliary information describing the new classes. Ultimately, this may allow to use textbook knowledge that humans employ to learn about new classes by transferring knowledge from classes they know well. The most successful zero-shot learning approaches currently require a particular type of auxiliary information -- namely attribute annotations performed by humans -- that is not readily available for most classes. Our goal is to circumvent this bottleneck by substituting such annotations by extracting multiple pieces of information from multiple unstructured text sources readily available on the web. To compensate for the weaker form of auxiliary information, we incorporate stronger supervision in the form of semantic part annotations on the classes from which we transfer knowledge. We achieve our goal by a joint embedding framework that maps multiple text parts as well as multiple semantic parts into a common space. Our results consistently and significantly improve on the state-of-the-art in zero-short recognition and retrieval.

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 "semantically tied paired cycle consistency for zero shot sketch based image retrieval"):
  with members:
    - "deep cross modality adaptation via semantics preserving adversarial learning for sketch based 3d shape retrieval"
    - "zero shot sketch image hashing"
    - "a zero shot framework for sketch based image retrieval"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "deep sketch hashing fast free hand sketch based image retrieval"

Connected through hyperedge HE-3 (induced by source paper "semantic guided multi attention localization for zero shot learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "transductive zero shot learning with visual structure constraint"
    - "moment matching for multi source domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "normface l 2 hypersphere embedding for face verification"

Connected through hyperedge HE-4 (induced by source paper "zero shot learning with the isoperimetric loss"):
  with members:
    - "zero shot kernel learning"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "learning to compare relation network for few shot learning"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "zero shot learning the good the bad and the ugly"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "out of distribution detection for generalized zero shot action recognition"):
  with members:
    - "visual data synthesis via gan for zero shot video classification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"

Connected through hyperedge HE-6 (induced by source paper "learning where to look semantic guided multi attention localization for zero shot learning"):
  with members:
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "moment matching for multi source domain adaptation"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "prototypical networks for few shot learning"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "semantically consistent regularization for zero shot recognition"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-7 (induced by source paper "zero shot feature selection via exploiting semantic knowledge"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "unsupervised feature selection with adaptive structure learning"
    - "zero shot recognition with unreliable attributes"

Connected through hyperedge HE-8 (induced by source paper "zero shot object detection learning to simultaneously recognize and localize novel concepts"):
  with members:
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"
    - "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"

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)