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: "feature generating networks for zero shot learning"
  Abstract: Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets -- CUB, FLO, SUN, AWA and ImageNet -- in both the zero-shot learning and generalized zero-shot learning settings.

Connected through hyperedge HE-1 (induced by source paper "latent embedding feedback and discriminative features for zero shot classification"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "semantically aligned bias reducing zero shot learning"
    - "out of distribution detection for generalized zero shot action recognition"
    - "transferable contrastive network for generalized zero shot learning"
    - "feedback network for image super resolution"
    - "generative dual adversarial network for generalized zero shot learning"
    - "towards universal representation for unseen action recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "any shot object detection"):
  with members:
    - "anchor loss modulating loss scale based on prediction difficulty"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few example object detection with model communication"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "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"

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"
    - "semantic autoencoder 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"
    - "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"
    - "semantic autoencoder for zero shot learning"
    - "learning a deep embedding model for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "boosting supervision with self supervision for few shot learning"
    - "moments in time dataset one million videos for event understanding"

Connected through hyperedge HE-6 (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-7 (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"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "zero shot learning via class conditioned deep generative models"

Connected through hyperedge HE-8 (induced by source paper "zero shot generation of human object interaction videos"):
  with members:
    - "video relationship reasoning using gated spatio temporal energy graph"
    - "cgans with projection discriminator"
    - "recycle gan unsupervised video retargeting"
    - "spectral normalization for generative adversarial networks"
    - "probabilistic video generation using holistic attribute control"
    - "large scale gan training for high fidelity natural image synthesis"
    - "videos as space time region graphs"
    - "image generation from scene graphs"

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)