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: "learning compositional representations for few shot recognition"
  Abstract: One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from just a few examples. Instrumental to this rapid learning ability is the compositional structure of concept representations in the human brain --- something that deep learning models are lacking. In this work, we make a step towards bridging this gap between human and machine learning by introducing a simple regularization technique that allows the learned representation to be decomposable into parts. Our method uses category-level attribute annotations to disentangle the feature space of a network into subspaces corresponding to the attributes. These attributes can be either purely visual, like object parts, or more abstract, like openness and symmetry. We demonstrate the value of compositional representations on three datasets: CUB-200-2011, SUN397, and ImageNet, and show that they require fewer examples to learn classifiers for novel categories.

Connected through hyperedge HE-1 (induced by source paper "incremental few shot object detection"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "boosting few shot visual learning with self supervision"
    - "large scale long tailed recognition in an open world"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "zero shot detection"
    - "a closer look at few shot classification"
    - "continual learning a comparative study on how to defy forgetting in classification tasks"
    - "objects as points"

Connected through hyperedge HE-2 (induced by source paper "towards fair cross domain adaptation via generative learning"):
  with members:
    - "diversity with cooperation ensemble methods for few shot classification"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "deep visual domain adaptation a survey"
    - "partial adversarial domain adaptation"
    - "bagan data augmentation with balancing gan"
    - "low shot learning from imaginary data"
    - "the frontiers of fairness in machine learning"

Connected through hyperedge HE-3 (induced by source paper "locality and compositionality in zero shot learning"):
  with members:
    - "visual space optimization for zero shot learning"
    - "measuring compositionality in representation learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning representations by maximizing mutual information across views"
    - "are few shot learning benchmarks too simple"
    - "centroid networks for few shot clustering and unsupervised few shot classification"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "large scale fine grained categorization and domain specific transfer learning"

Connected through hyperedge HE-4 (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"
    - "adaptive cross modal few shot learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "adaptive cross modal few shot learning"):
  with members:
    - "meta learning with latent embedding optimization"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "bayesian model agnostic meta learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "zero and few shot learning with semantic feature synthesis and competitive learning"
    - "low shot learning from imaginary data"
    - "probabilistic model agnostic meta learning"
    - "learning robust visual semantic embeddings"

Connected through hyperedge HE-6 (induced by source paper "learning generalizable representations via diverse supervision"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "boosting few shot visual learning with self supervision"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "a closer look at few shot classification"
    - "many task learning with task routing"
    - "meta learning for semi supervised few shot classification"
    - "multi task learning as multi objective optimization"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-7 (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"
    - "adaptive cross modal few shot learning"
    - "grounding visual explanations"
    - "e snli natural language inference with natural language explanations"
    - "learning to understand goal specifications by modelling reward"

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)