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 embedding adaptation for few shot learning"
  Abstract: Learning with limited data is a key challenge for visual recognition. Few-shot learning methods address this challenge by learning an instance embedding function from seen classes and apply the function to instances from unseen classes with limited labels. This style of transfer learning is task-agnostic: the embedding function is not learned optimally discriminative with respect to the unseen classes, where discerning among them is the target task. In this paper, we propose a novel approach to adapt the embedding model to the target classification task, yielding embeddings that are task-specific and are discriminative. To this end, we employ a type of self-attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by focusing on relating instances from the test instances to the training instances in both seen and unseen classes. We verify the effectiveness of our model on both the standard few-shot classification benchmark and four extended few-shot learning settings with essential use cases (i.e. cross-domain, transductive, generalized few-shot learning, and large scale low-shot learning). Our approach archived consistent improvements over baseline models and previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "few shot few shot learning and the role of spatial attention"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "large scale long tailed recognition in an open world"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-3 (induced by source paper "exploiting unsupervised inputs for accurate few shot classification"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "generalizing from a few examples a survey on few shot learning"
    - "prototype rectification for few shot learning"
    - "charting the right manifold manifold mixup for few shot learning"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "few shot learning as domain adaptation algorithm and analysis"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "charting the right manifold manifold mixup for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "a closer look at few shot classification"
    - "adaptive cross modal few shot learning"
    - "set transformer a framework for attention based permutation invariant neural networks"

Connected through hyperedge HE-5 (induced by source paper "meta learning across meta tasks for few shot learning"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "learning what and where to transfer"
    - "charting the right manifold manifold mixup for few shot learning"
    - "infinite mixture prototypes for few shot learning"
    - "on minimum discrepancy estimation for deep domain adaptation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "bridging theory and algorithm for domain adaptation"
    - "transductive episodic wise adaptive metric for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "novelty prepared few shot classification"):
  with members:
    - "deep high resolution representation learning for visual recognition"
    - "a closer look at few shot classification"
    - "learning to learn via self critique"
    - "deep high resolution representation learning for human pose estimation"
    - "few shot learning with metric agnostic conditional embeddings"
    - "meta learning with latent embedding optimization"
    - "deep comparison relation columns for few shot learning"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-7 (induced by source paper "learning for new visual environments with limited labels"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "domain invariant projection learning for zero shot recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "generative dual adversarial network for generalized zero shot learning"
    - "meta learning with latent embedding optimization"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "improved techniques for adversarial discriminative domain adaptation"
    - "discriminative learning of latent features for zero shot recognition"

Connected through hyperedge HE-8 (induced by source paper "multimodal model agnostic meta learning via task aware modulation"):
  with members:
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "on first order meta learning algorithms"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "meta continual learning"
    - "recasting gradient based meta learning as hierarchical bayes"

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.LG(Machine Learning)