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: "rapid adaptation with conditionally shifted neurons"
  Abstract: We describe a mechanism by which artificial neural networks can learn rapid adaptation - the ability to adapt on the fly, with little data, to new tasks - that we call conditionally shifted neurons. We apply this mechanism in the framework of metalearning, where the aim is to replicate some of the flexibility of human learning in machines. Conditionally shifted neurons modify their activation values with task-specific shifts retrieved from a memory module, which is populated rapidly based on limited task experience. On metalearning benchmarks from the vision and language domains, models augmented with conditionally shifted neurons achieve state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "a two stage approach to few shot learning for image recognition"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "meta curvature"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"
    - "autoaugment learning augmentation policies from data"
    - "low shot learning from imaginary data"
    - "promp proximal meta policy search"

Connected through hyperedge HE-5 (induced by source paper "a new meta baseline for few shot learning"):
  with members:
    - "tafe net task aware feature embeddings for low shot learning"
    - "a closer look at few shot classification"
    - "meta learning for semi supervised few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "dropblock a regularization method for convolutional networks"
    - "meta transfer learning for few shot learning"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-6 (induced by source paper "pac bayesian meta learning with implicit prior"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "probabilistic model agnostic meta learning"
    - "on calibration of modern neural networks"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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-8 (induced by source paper "meta transfer learning through hard tasks"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "a closer look at few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few 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.LG(Machine Learning)