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: "model selection for generalized zero shot learning"
  Abstract: In the problem of generalized zero-shot learning, the datapoints from unknown classes are not available during training. The main challenge for generalized zero-shot learning is the unbalanced data distribution which makes it hard for the classifier to distinguish if a given testing sample comes from a seen or unseen class. However, using Generative Adversarial Network (GAN) to generate auxiliary datapoints by the semantic embeddings of unseen classes alleviates the above problem. Current approaches combine the auxiliary datapoints and original training data to train the generalized zero-shot learning model and obtain state-of-the-art results. Inspired by such models, we propose to feed the generated data via a model selection mechanism. Specifically, we leverage two sources of datapoints (observed and auxiliary) to train some classifier to recognize which test datapoints come from seen and which from unseen classes. This way, generalized zero-shot learning can be divided into two disjoint classification tasks, thus reducing the negative influence of the unbalanced data distribution. Our evaluations on four publicly available datasets for generalized zero-shot learning show that our model obtains state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "few shot learning via saliency guided hallucination of samples"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "zero shot kernel learning"
    - "a deeper look at power normalizations"
    - "low shot learning from imaginary data"
    - "power normalizing second order similarity network for few shot learning"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (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"
    - "transductive unbiased embedding for zero shot learning"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-3 (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"
    - "transductive unbiased embedding for zero shot learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"

Connected through hyperedge HE-4 (induced by source paper "power normalizing second order similarity network for few shot learning"):
  with members:
    - "zero shot kernel learning"
    - "a deeper look at power normalizations"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"
    - "feature generating networks for zero shot learning"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "matching networks for one shot learning"

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)