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: "incremental few shot learning with attention attractor networks"
  Abstract: Machine learning classifiers are often trained to recognize a set of pre-defined classes. However, in many applications, it is often desirable to have the flexibility of learning additional concepts, with limited data and without re-training on the full training set. This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples. After learning the novel classes, the model is then evaluated on the overall classification performance on both base and novel classes. To this end, we propose a meta-learning model, the Attention Attractor Network, which regularizes the learning of novel classes. In each episode, we train a set of new weights to recognize novel classes until they converge, and we show that the technique of recurrent back-propagation can back-propagate through the optimization process and facilitate the learning of these parameters. We demonstrate that the learned attractor network can help recognize novel classes while remembering old classes without the need to review the original training set, outperforming various baselines.

Connected through hyperedge HE-1 (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-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"
    - "adaptive cross modal few shot learning"
    - "set transformer a framework for attention based permutation invariant neural networks"

Connected through hyperedge HE-3 (induced by source paper "incremental learning for metric based meta learners"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "online meta learning"
    - "incremental few shot learning for pedestrian attribute recognition"
    - "few shot learning with embedded class models and shot free meta training"
    - "meta learning with latent embedding optimization"
    - "efficient lifelong learning with a gem"
    - "dynamic few shot visual learning without forgetting"
    - "the devil is in the tails fine grained classification in the wild"

Connected through hyperedge HE-4 (induced by source paper "xtarnet learning to extract task adaptive representation for incremental few shot learning"):
  with members:
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "continual learning with deep generative replay"
    - "a simple neural attentive meta learner"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-5 (induced by source paper "large scale long tailed recognition in an open world"):
  with members:
    - "model cards for model reporting"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "women also snowboard overcoming bias in captioning models"
    - "arcface additive angular margin loss for deep face recognition"
    - "episodic curiosity through reachability"
    - "low shot learning from imaginary data"
    - "learning adversarially fair and transferable representations"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-6 (induced by source paper "learning adaptive classifiers synthesis for generalized few shot learning"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "finding task relevant features for few shot learning by category traversal"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "a closer look at few shot classification"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-7 (induced by source paper "protoattend attention based prototypical learning"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representer point selection for explaining deep neural networks"
    - "this looks like that deep learning for interpretable image recognition"
    - "deep anomaly detection with outlier exposure"
    - "catboost gradient boosting with categorical features support"
    - "attention based ensemble for deep metric learning"
    - "bayesian neural networks"

Connected through hyperedge HE-8 (induced by source paper "finding task relevant features for few shot learning by category traversal"):
  with members:
    - "a closer look at few shot classification"
    - "image deformation meta networks for one shot learning"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "neural network encapsulation"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "improving generalization via scalable neighborhood component analysis"
    - "dynamic few shot visual learning without forgetting"

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)