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 cross modal few shot learning"
  Abstract: Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance metric-based few-shot learning methods. Visual and semantic feature spaces have different structures by definition. For certain concepts, visual features might be richer and more discriminative than text ones. While for others, the inverse might be true. Moreover, when the support from visual information is limited in image classification, semantic representations (learned from unsupervised text corpora) can provide strong prior knowledge and context to help learning. Based on these two intuitions, we propose a mechanism that can adaptively combine information from both modalities according to new image categories to be learned. Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested. Experiments also show that our model can effectively adjust its focus on the two modalities. The improvement in performance is particularly large when the number of shots is very small.

Connected through hyperedge HE-1 (induced by source paper "tafssl task adaptive feature sub space learning for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "multi level semantic feature augmentation for one shot learning"
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "laso label set operations networks for multi label few shot learning"

Connected through hyperedge HE-2 (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"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-3 (induced by source paper "embedding propagation smoother manifold for few shot classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "charting the right manifold manifold mixup for few shot learning"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-4 (induced by source paper "metadapt meta learned task adaptive architecture for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "few example object detection with model communication"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "laso label set operations networks for multi label few shot learning"

Connected through hyperedge HE-5 (induced by source paper "baby steps towards few shot learning with multiple semantics"):
  with members:
    - "simpleshot revisiting nearest neighbor classification for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "few example object detection with model communication"
    - "a closer look at few shot classification"
    - "metadapt meta learned task adaptive architecture for few shot classification"
    - "meta learning with latent embedding optimization"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "deep meta learning learning to learn in the concept space"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised compositional featureaggregation for few shot recognition"):
  with members:
    - "measuring compositionality in representation learning"
    - "generalizing from a few examples a survey on few shot learning"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "few shot object detection via feature reweighting"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "learning compositional representations for few shot recognition"

Connected through hyperedge HE-7 (induced by source paper "few shot learning with contextual cueing for object recognition in complex scenes"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "dense classification and implanting for few shot learning"
    - "context aware zero shot learning for object recognition"
    - "few shot learning via saliency guided hallucination of samples"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "tafe net task aware feature embeddings for low shot learning"
    - "image deformation meta networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "shaping visual representations with language for few shot classification"):
  with members:
    - "baby steps towards few shot learning with multiple semantics"
    - "using natural language for reward shaping in reinforcement learning"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "a closer look at few shot classification"
    - "grounding visual explanations"
    - "e snli natural language inference with natural language explanations"
    - "learning to understand goal specifications by modelling reward"
    - "learning compositional representations for few shot 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.LG(Machine Learning)