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: "write a classifier predicting visual classifiers from unstructured text"
  Abstract: People typically learn through exposure to visual concepts associated with linguistic descriptions. For instance, teaching visual object categories to children is often accompanied by descriptions in text or speech. In a machine learning context, these observations motivates us to ask whether this learning process could be computationally modeled to learn visual classifiers. More specifically, the main question of this work is how to utilize purely textual description of visual classes with no training images, to learn explicit visual classifiers for them. We propose and investigate two baseline formulations, based on regression and domain transfer, that predict a linear classifier. Then, we propose a new constrained optimization formulation that combines a regression function and a knowledge transfer function with additional constraints to predict the parameters of a linear classifier. We also propose a generic kernelized models where a kernel classifier is predicted in the form defined by the representer theorem. The kernelized models allow defining and utilizing any two RKHS (Reproducing Kernel Hilbert Space) kernel functions in the visual space and text space, respectively. We finally propose a kernel function between unstructured text descriptions that builds on distributional semantics, which shows an advantage in our setting and could be useful for other applications. We applied all the studied models to predict visual classifiers on two fine-grained and challenging categorization datasets (CU Birds and Flower Datasets), and the results indicate successful predictions of our final model over several baselines that we designed.

Connected through hyperedge HE-1 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling"):
  with members:
    - "object driven text to image synthesis via adversarial training"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "text adaptive generative adversarial networks manipulating images with natural language"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "recent advances in zero shot recognition"
    - "self supervised learning of visual features through embedding images into text topic spaces"

Connected through hyperedge HE-2 (induced by source paper "creativity inspired zero shot learning"):
  with members:
    - "design design inspiration from generative networks"
    - "multi modal cycle consistent generalized zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "learning robust visual semantic embeddings"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "a generative adversarial approach for zero shot learning from noisy texts"

Connected through hyperedge HE-3 (induced by source paper "canzsl cycle consistent adversarial networks for zero shot learning from natural language"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "cycada cycle consistent adversarial domain adaptation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "camera style adaptation for person re identification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "large scale visual relationship understanding"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "introduction to the 1st place winning model of openimages relationship detection challenge"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "an interpretable model for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-5 (induced by source paper "choose your neuron incorporating domain knowledge through neuron importance"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding convolutional networks with apple automatic patch pattern labeling for explanation"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "network dissection quantifying interpretability of deep visual representations"
    - "nisp pruning networks using neuron importance score propagation"
    - "zero shot learning the good the bad and the ugly"
    - "less is more zero shot learning from online textual documents with noise suppression"

Connected through hyperedge HE-6 (induced by source paper "efficient lifelong learning with a gem"):
  with members:
    - "modular meta learning"
    - "reinforced continual learning"
    - "large scale visual relationship understanding"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "automatically composing representation transformations as a means for generalization"
    - "memory aware synapses learning what not to forget"
    - "progressive neural networks"
    - "routing networks adaptive selection of non linear functions for multi task learning"

Connected through hyperedge HE-7 (induced by source paper "a generative adversarial approach for zero shot learning from noisy texts"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "densely connected pyramid dehazing network"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "learning robust visual semantic embeddings"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"

Connected through hyperedge HE-8 (induced by source paper "link the head to the beak zero shot learning from noisy text description at part precision"):
  with members:
    - "write a classifier predicting visual classifiers from unstructured text descriptions"
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"
    - "multi cue zero shot learning with strong supervision"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "learning hypergraph regularized attribute predictors"

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)