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: "zero shot recognition via semantic embeddings and knowledge graphs"
  Abstract: We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this paper, we build upon the recently introduced Graph Convolutional Network (GCN) and propose an approach that uses both semantic embeddings and the categorical relationships to predict the classifiers. Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category). After a series of graph convolutions, we predict the visual classifier for each category. During training, the visual classifiers for a few categories are given to learn the GCN parameters. At test time, these filters are used to predict the visual classifiers of unseen categories. We show that our approach is robust to noise in the KG. More importantly, our approach provides significant improvement in performance compared to the current state-of-the-art results (from 2 ~ 3% on some metrics to whopping 20% on a few).

Connected through hyperedge HE-1 (induced by source paper "relationship embedded representation learning for grounding referring expressions"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "cross modal relationship inference for grounding referring expressions"
    - "murel multimodal relational reasoning for visual question answering"
    - "interpretable visual question answering by visual grounding from attention supervision mining"
    - "a neural compositional paradigm for image captioning"
    - "graph r cnn for scene graph generation"
    - "contrast oriented deep neural networks for salient object detection"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "bridging knowledge graphs to generate scene graphs"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "entity relation and event extraction with contextualized span representations"
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning by abstraction the neural state machine"
    - "an empirical study on leveraging scene graphs for visual question answering"
    - "class balanced loss based on effective number of samples"
    - "question answering as global reasoning over semantic abstractions"
    - "linknet relational embedding for scene graph"

Connected through hyperedge HE-3 (induced by source paper "differentiable graph module dgm graph convolutional networks"):
  with members:
    - "brain signal classification via learning connectivity structure"
    - "graph neural networks with convolutional arma filters"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "link prediction based on graph neural networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "adaptive graph convolutional neural networks"
    - "dual primal graph convolutional networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-4 (induced by source paper "universal rcnn universal object detector via transferable graph r cnn"):
  with members:
    - "upsnet a unified panoptic segmentation network"
    - "feature selective anchor free module for single shot object detection"
    - "iterative visual reasoning beyond convolutions"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "taskonomy disentangling task transfer learning"
    - "frage frequency agnostic word representation"
    - "detnet a backbone network for object detection"
    - "hybrid knowledge routed modules for large scale object detection"

Connected through hyperedge HE-5 (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"
    - "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"
    - "generalized zero shot recognition based on visually semantic embedding"

Connected through hyperedge HE-6 (induced by source paper "an ontology aware framework for audio event classification"):
  with members:
    - "improving image captioning by leveraging knowledge graphs"
    - "multi label image recognition with graph convolutional networks"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "graph convolutional networks for text classification"
    - "geometric deep learning going beyond euclidean data"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "cnn architectures for large scale audio classification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "bayesian relational memory for semantic visual navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "benchmarking classic and learned navigation in complex 3d environments"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "composable planning with attributes"
    - "iterative visual reasoning beyond convolutions"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"

Connected through hyperedge HE-8 (induced by source paper "an optimal transport framework for zero shot learning"):
  with members:
    - "improving textual network embedding with global attention via optimal transport"
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "discriminative learning of latent features for zero shot recognition"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "zero shot kernel learning"
    - "improving gans using optimal transport"

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)