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: "learning joint feature adaptation for zero shot recognition"
  Abstract: Zero-shot recognition (ZSR) aims to recognize target-domain data instances of unseen classes based on the models learned from associated pairs of seen-class source and target domain data. One of the key challenges in ZSR is the relative scarcity of source-domain features (e.g. one feature vector per class), which do not fully account for wide variability in target-domain instances. In this paper we propose a novel framework of learning data-dependent feature transforms for scoring similarity between an arbitrary pair of source and target data instances to account for the wide variability in target domain. Our proposed approach is based on optimizing over a parameterized family of local feature displacements that maximize the source-target adaptive similarity functions. Accordingly we propose formulating zero-shot learning (ZSL) using latent structural SVMs to learn our similarity functions from training data. As demonstration we design a specific algorithm under the proposed framework involving bilinear similarity functions and regularized least squares as penalties for feature displacement. We test our approach on several benchmark datasets for ZSR and show significant improvement over the state-of-the-art. For instance, on aP&Y dataset we can achieve 80.89% in terms of recognition accuracy, outperforming the state-of-the-art by 11.15%.

Connected through hyperedge HE-1 (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"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a generative framework for zero shot learning with adversarial domain adaptation"):
  with members:
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "transductive unbiased embedding for zero shot learning"
    - "attribute based synthetic network abs net learning more from pseudo feature representations"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "cycada cycle consistent adversarial domain adaptation"
    - "zero shot learning via class conditioned deep generative models"

Connected through hyperedge HE-3 (induced by source paper "zero shot learning via class conditioned deep generative models"):
  with members:
    - "semantic autoencoder for zero shot learning"
    - "zero shot learning with generative latent prototype model"
    - "transductive zero shot learning with a self training dictionary approach"
    - "a simple exponential family framework for zero shot learning"
    - "learning robust visual semantic embeddings"
    - "deconvolutional latent variable model for text sequence matching"
    - "transductive zero shot learning with adaptive structural embedding"
    - "zero shot learning posed as a missing data problem"

Connected through hyperedge HE-4 (induced by source paper "a simple exponential family framework for zero shot learning"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "zero shot learning posed as a missing data problem"
    - "learning a deep embedding model for zero shot learning"
    - "synthesized classifiers for zero shot learning"
    - "zero shot visual recognition via bidirectional latent embedding"
    - "improving semantic embedding consistency by metric learning for zero shot classification"
    - "an empirical study and analysis of generalized zero shot learning for object recognition in the wild"
    - "latent embeddings for zero shot classification"

Connected through hyperedge HE-5 (induced by source paper "generalized zero shot learning via synthesized examples"):
  with members:
    - "toward controlled generation of text"
    - "semantic autoencoder for zero shot learning"
    - "a simple exponential family framework for zero shot learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "zero shot learning via class conditioned deep generative models"
    - "a generative model for zero shot learning using conditional variational autoencoders"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "zero shot learning the good the bad and the ugly"

Connected through hyperedge HE-6 (induced by source paper "attributes2classname a discriminative model for attribute based unsupervised zero shot learning"):
  with members:
    - "recovering the missing link predicting class attribute associations for unsupervised zero shot learning"
    - "multi cue zero shot learning with strong supervision"
    - "yfcc100m the new data in multimedia research"
    - "latent embeddings for zero shot classification"
    - "how to transfer zero shot object recognition via hierarchical transfer of semantic attributes"
    - "the new data and new challenges in multimedia research"
    - "zero shot learning via semantic similarity embedding"
    - "objects2action classifying and localizing actions without any video example"

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)