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 zero shot learning with a self training dictionary approach"
  Abstract: As an important and challenging problem in computer vision, zero-shot learning (ZSL) aims at automatically recognizing the instances from unseen object classes without training data. To address this problem, ZSL is usually carried out in the following two aspects: 1) capturing the domain distribution connections between seen classes data and unseen classes data; and 2) modeling the semantic interactions between the image feature space and the label embedding space. Motivated by these observations, we propose a bidirectional mapping based semantic relationship modeling scheme that seeks for crossmodal knowledge transfer by simultaneously projecting the image features and label embeddings into a common latent space. Namely, we have a bidirectional connection relationship that takes place from the image feature space to the latent space as well as from the label embedding space to the latent space. To deal with the domain shift problem, we further present a transductive learning approach that formulates the class prediction problem in an iterative refining process, where the object classification capacity is progressively reinforced through bootstrapping-based model updating over highly reliable instances. Experimental results on three benchmark datasets (AwA, CUB and SUN) demonstrate the effectiveness of the proposed approach against the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "transfer adaptation learning a decade survey"):
  with members:
    - "optimal projection guided transfer hashing for image retrieval"
    - "representation learning by rotating your faces"
    - "open set domain adaptation for image and action recognition"
    - "deep visual domain adaptation a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "load balanced gans for multi view face image synthesis"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot learning for 3d point cloud classification"):
  with members:
    - "deeppoint3d learning discriminative local descriptors using deep metric learning on 3d point clouds"
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "mitigating the hubness problem for zero shot learning of 3d objects"
    - "representation learning on unit ball with 3d roto translational equivariance"
    - "zero shot learning of 3d point cloud objects"
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "deep multiple instance learning for zero shot image tagging"
    - "domain invariant projection learning for zero shot recognition"

Connected through hyperedge HE-3 (induced by source paper "hierarchical prototype learning for zero shot recognition"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "discriminative learning of latent features for zero shot recognition"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-4 (induced by source paper "meta transfer networks for zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
    - "zero shot learning via class conditioned deep generative models"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "learning to compare relation network for few shot learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "zero and few shot learning with semantic feature synthesis and competitive learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "low shot learning from imaginary data"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "low shot learning with large scale diffusion"
    - "learning to learn meta critic networks for sample efficient learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "domain invariant projection learning for zero shot recognition"):
  with members:
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-7 (induced by source paper "polarity loss for zero shot object detection"):
  with members:
    - "zero shot detection"
    - "deep multiple instance learning for zero shot image tagging"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "zero shot object detection by hybrid region embedding"
    - "deep feature pyramid reconfiguration for object detection"
    - "transductive unbiased embedding for zero shot learning"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-8 (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"
    - "zero shot learning via class conditioned deep generative models"

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)