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: "piecewise classifier mappings learning fine grained learners for novel categories with few examples"
  Abstract: Humans are capable of learning a new fine-grained concept with very little supervision, e.g., few exemplary images for a species of bird, yet our best deep learning systems need hundreds or thousands of labeled examples. In this paper, we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting, termed few-shot fine-grained recognition (FSFG). The task of FSFG requires the learning systems to build classifiers for the novel fine-grained categories from few examples (only one or less than five). To solve this problem, we propose an end-to-end trainable deep network, which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task. Specifically, our network consists of a bilinear feature learning module and a classifier mapping module: while the former encodes the discriminative information of an exemplar image into a feature vector, the latter maps the intermediate feature into the decision boundary of the novel category. The key novelty of our model is a “piecewise mappings” function in the classifier mapping module, which generates the decision boundary via learning a set of more attainable sub-classifiers in a more parameter-economic way. We learn the exemplar-to-classifier mapping based on an auxiliary dataset in a meta-learning fashion, which is expected to be able to generalize to novel categories. By conducting comprehensive experiments on three fine-grained datasets, we demonstrate that the proposed method achieves superior performance over the competing baselines.

Connected through hyperedge HE-1 (induced by source paper "deep learning for fine grained image analysis a survey"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "multi attention multi class constraint for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "coarse to fine a rnn based hierarchical attention model for vehicle re identification"
    - "part aligned bilinear representations for person re identification"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "meta transfer networks for zero shot learning"):
  with members:
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "transductive zero shot learning with a self training dictionary approach"
    - "zero shot learning via class conditioned deep generative models"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "a generative adversarial approach for zero shot learning from noisy texts"
    - "learning to compare relation network for few shot learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "compare more nuanced pairwise alignment bilinear network for few shot fine grained learning"):
  with members:
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "cross modal hallucination for few shot fine grained recognition"
    - "one shot fine grained instance retrieval"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "the inaturalist species classification and detection dataset"
    - "matching networks for one shot learning"

Connected through hyperedge HE-4 (induced by source paper "learning embedding adaptation for few shot learning"):
  with members:
    - "universal transformers"
    - "on first order meta learning algorithms"
    - "image transformer"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "unsupervised learning via meta 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)