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: "how to transfer zero shot object recognition via hierarchical transfer of semantic attributes"
  Abstract: Attribute based knowledge transfer has proven very successful in visual object analysis and learning previously unseen classes. However, the common approach learns and transfers attributes without taking into consideration the embedded structure between the categories in the source set. Such information provides important cues on the intraattribute variations. We propose to capture these variations in a hierarchical model that expands the knowledge source with additional abstraction levels of attributes. We also provide a novel transfer approach that can choose the appropriate attributes to be shared with an unseen class. We evaluate our approach on three public datasets: a Pascal, Animals with Attributes and CUB-200-2011 Birds. The experiments demonstrate the effectiveness of our model with significant improvement over state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "recognizing part attributes with insufficient data"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "a large scale attribute dataset for zero shot learning"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "pseudo mask augmented object detection"
    - "cbam convolutional block attention module"
    - "non local neural networks"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "attribute recognition by joint recurrent learning of context and correlation"

Connected through hyperedge HE-2 (induced by source paper "learning visually consistent label embeddings for zero shot learning"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "multi modal cycle consistent generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"

Connected through hyperedge HE-3 (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-4 (induced by source paper "domain aware generalized zero shot learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "multi modal cycle consistent generalized zero shot learning"
    - "zero shot kernel learning"
    - "preserving semantic relations for zero shot learning"
    - "probabilistic and or attribute grouping for zero shot learning"
    - "model selection for generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "adaptive confidence smoothing for generalized zero shot learning"):
  with members:
    - "domain invariant projection learning for zero shot recognition"
    - "multi modal cycle consistent generalized zero shot learning"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "zero shot kernel learning"
    - "probabilistic and or attribute grouping for zero shot learning"
    - "model selection for generalized zero shot learning"
    - "transductive unbiased embedding for zero shot learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-6 (induced by source paper "zero shot object detection by hybrid region embedding"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "transductive unbiased embedding for zero shot learning"
    - "zero shot object detection"
    - "semantic autoencoder for zero shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-7 (induced by source paper "probabilistic and or attribute grouping for zero shot learning"):
  with members:
    - "recent advances in zero shot recognition"
    - "context aware captions from context agnostic supervision"
    - "learning robust visual semantic embeddings"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "semantically consistent regularization for zero shot recognition"
    - "zero shot recognition using dual visual semantic mapping paths"
    - "feature generating networks for zero shot learning"

Connected through hyperedge HE-8 (induced by source paper "transductive zero shot learning with adaptive structural embedding"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "semi supervised zero shot learning by a clustering based approach"
    - "synthesized classifiers for zero shot learning"
    - "convolution in convolution for network in network"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "semi supervised vocabulary informed learning"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "latent embeddings for zero shot 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)