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: "adaptive confidence smoothing for generalized zero shot learning"
  Abstract: Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a single model that operates in these two regimes simultaneously is challenging. Here we describe a probabilistic approach that breaks the model into three modular components, and then combines them in a consistent way. Specifically, our model consists of three classifiers: A "gating" model that makes soft decisions if a sample is from a "seen" class, and two experts: a ZSL expert, and an expert model for seen classes. #R##N#We address two main difficulties in this approach: How to provide an accurate estimate of the gating probability without any training samples for unseen classes; and how to use expert predictions when it observes samples outside of its domain. The key insight to our approach is to pass information between the three models to improve each one's accuracy, while maintaining the modular structure. We test our approach, adaptive confidence smoothing (COSMO), on four standard GZSL benchmark datasets and find that it largely outperforms state-of-the-art GZSL models. COSMO is also the first model that closes the gap and surpasses the performance of generative models for GZSL, even-though it is a light-weight model that is much easier to train and tune. #R##N#Notably, COSMO offers a new view for developing zero-shot models. Thanks to COSMO's modular structure, instead of trying to perform well both on seen and on unseen classes, models can focus on accurate classification of unseen classes, and later consider seen class models.

Connected through hyperedge HE-1 (induced by source paper "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"

Connected through hyperedge HE-2 (induced by source paper "a semantics guided class imbalance learning model for zero shot classification"):
  with members:
    - "multi modal cycle consistent generalized zero shot learning"
    - "learning class prototypes via structure alignment for zero shot recognition"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "visual and semantic prototypes jointly guided cnn for generalized zero shot learning"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "reducing network agnostophobia"
    - "robust classification with convolutional prototype learning"
    - "zero shot kernel learning"
    - "towards effective deep embedding for zero shot learning"
    - "bi adversarial auto encoder for zero shot learning"

Connected through hyperedge HE-4 (induced by source paper "clarel classification via retrieval loss for zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "zero shot image recognition using relational matching adaptation and calibration"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "multi modal cycle consistent generalized zero shot learning"
    - "probabilistic and or attribute grouping for zero shot learning"
    - "learning robust visual semantic embeddings"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"

Connected through hyperedge HE-5 (induced by source paper "generalised zero shot learning with domain classification in a joint semantic and visual space"):
  with members:
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "multi modal cycle consistent generalized zero shot learning"
    - "semantic autoencoder for zero shot learning"
    - "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"
    - "feature generating networks for zero shot learning"
    - "label embedding for image classification"
    - "synthesized classifiers for zero shot learning"

Connected through hyperedge HE-6 (induced by source paper "few shot learning with per sample rich supervision"):
  with members:
    - "teaching categories to human learners with visual explanations"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "zero shot learning the good the bad and the ugly"
    - "matching networks for one shot learning"
    - "low shot visual object recognition"
    - "low shot visual recognition by shrinking and hallucinating features"

Connected through hyperedge HE-7 (induced by source paper "generalised zero shot learning with a classifier ensemble over multi modal embedding spaces"):
  with members:
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "semantic autoencoder for zero shot learning"
    - "on calibration of modern neural networks"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)