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 posterior learning few shot learning with a surprise based memory module"
  Abstract: The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.

Connected through hyperedge HE-1 (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"
    - "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"
    - "meta learning with differentiable closed form solvers"

Connected through hyperedge HE-2 (induced by source paper "learning to compare relation semantic alignment for few shot learning"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "dense classification and implanting for few shot learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "compare more nuanced pairwise alignment bilinear network for few shot fine grained learning"

Connected through hyperedge HE-3 (induced by source paper "few shot learning with global class representations"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"
    - "memory matching networks for one shot image recognition"
    - "dynamic few shot visual learning without forgetting"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-4 (induced by source paper "episodic memory in lifelong language learning"):
  with members:
    - "sentence embedding alignment for lifelong relation extraction"
    - "learning and evaluating general linguistic intelligence"
    - "the natural language decathlon multitask learning as question answering"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "efficient lifelong learning with a gem"
    - "deep contextualized word representations"
    - "an empirical study of example forgetting during deep neural network learning"
    - "higher order coreference resolution with coarse to fine inference"

Connected through hyperedge HE-5 (induced by source paper "an empirical study of pretrained representations for few shot classification"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "do imagenet classifiers generalize to imagenet"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning for semi supervised few shot classification"
    - "meta learning with latent embedding optimization"
    - "feature denoising for improving adversarial robustness"
    - "conditional neural processes"

Connected through hyperedge HE-6 (induced by source paper "meta neighborhoods"):
  with members:
    - "meta curvature"
    - "self supervised generalisation with meta auxiliary learning"
    - "a closer look at few shot classification"
    - "robust classification with convolutional prototype learning"
    - "meta learning with differentiable closed form solvers"
    - "memory matching networks for one shot image recognition"
    - "prototypical networks for few shot learning"
    - "learning to remember rare events"

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)