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: "simpleshot revisiting nearest neighbor classification for few shot learning"
  Abstract: Few-shot learners aim to recognize new object classes based on a small number of labeled training examples. To prevent overfitting, state-of-the-art few-shot learners use meta-learning on convolutional-network features and perform classification using a nearest-neighbor classifier. This paper studies the accuracy of nearest-neighbor baselines without meta-learning. Surprisingly, we find simple feature transformations suffice to obtain competitive few-shot learning accuracies. For example, we find that a nearest-neighbor classifier used in combination with mean-subtraction and L2-normalization outperforms prior results in three out of five settings on the miniImageNet dataset.

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"
    - "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"
    - "fast autoaugment"

Connected through hyperedge HE-2 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning"):
  with members:
    - "a study on action detection in the wild"
    - "dynamic curriculum learning for imbalanced data classification"
    - "decoupling representation and classifier for long tailed recognition"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "large scale fine grained categorization and domain specific transfer learning"

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"
    - "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"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-4 (induced by source paper "baby steps towards few shot learning with multiple semantics"):
  with members:
    - "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"
    - "adaptive cross modal few shot learning"
    - "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-5 (induced by source paper "deep kernel transfer in gaussian processes for few shot learning"):
  with members:
    - "adaptive deep kernel learning"
    - "meta learning with implicit gradients"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reconciling meta learning and continual learning with online mixtures of tasks"
    - "learning embedding adaptation for few shot learning"
    - "how to train your maml"

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)