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: "low shot visual object recognition"
  Abstract: Low-shot visual learning - the ability to recognize novel object categories from very few examples - is a hallmark of human visual intelligence. Existing machine learning approaches fail to generalize in the same way. To make progress on this foundational problem, we present a novel protocol to evaluate low-shot learning on complex images where the learner is permitted to first build a feature representation. Then, we propose and evaluate representation regularization techniques that improve the effectiveness of convolutional networks at the task of low-shot learning, leading to a 2x reduction in the amount of training data required at equal accuracy rates on the challenging ImageNet dataset.

Connected through hyperedge HE-1 (induced by source paper "growing a brain fine tuning by increasing model capacity"):
  with members:
    - "knowledge transfer in deep block modular neural networks"
    - "progressive neural networks"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "a growing long term episodic semantic memory"
    - "one shot learning with memory augmented neural networks"
    - "learning feed forward one shot learners"
    - "cross stitch networks for multi task learning"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-2 (induced by source paper "generative one shot face recognition"):
  with members:
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "sphereface deep hypersphere embedding for face recognition"
    - "good semi supervised learning that requires a bad gan"
    - "few shot adversarial domain adaptation"
    - "one shot face recognition by promoting underrepresented classes"
    - "generative adversarial residual pairwise networks for one shot learning"
    - "range loss for deep face recognition with long tail"

Connected through hyperedge HE-3 (induced by source paper "protogan towards few shot learning for action recognition"):
  with members:
    - "a closer look at few shot classification"
    - "a generative approach to zero shot and few shot action recognition"
    - "multi modal cycle consistent generalized zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"
    - "matching networks for one shot learning"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-4 (induced by source paper "low shot learning from imaginary 3d model"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "learning category specific mesh reconstruction from image collections"
    - "low shot learning from imaginary data"
    - "low shot learning with large scale diffusion"
    - "prototypical networks for few shot learning"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "learning feed forward one shot learners"

Connected through hyperedge HE-5 (induced by source paper "few shot learning with per sample rich supervision"):
  with members:
    - "teaching categories to human learners with visual explanations"
    - "adaptive confidence smoothing for generalized zero shot learning"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "zero shot learning the good the bad and the ugly"
    - "matching networks for one shot learning"
    - "low shot visual recognition by shrinking and hallucinating features"

Connected through hyperedge HE-6 (induced by source paper "deep meta learning learning to learn in the concept space"):
  with members:
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "an analysis of deep neural network models for practical applications"
    - "prototypical networks for few shot learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to remember rare events"
    - "meta sgd learning to learn quickly for few shot learning"
    - "a simple neural attentive meta learner"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "memory matching networks for one shot image recognition"):
  with members:
    - "to create what you tell generating videos from captions"
    - "learning feed forward one shot learners"
    - "boosting image captioning with attributes"
    - "key value memory networks for directly reading documents"
    - "matching networks for one shot learning"
    - "jointly modeling embedding and translation to bridge video and language"
    - "end to end memory networks"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-8 (induced by source paper "sketch a classifier sketch based photo classifier generation"):
  with members:
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "learning to learn by gradient descent by gradient descent"
    - "learning a deep embedding model for zero shot learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "synthesized classifiers for zero shot learning"
    - "diet networks thin parameters for fat genomic"
    - "semi supervised vocabulary informed 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.CV(Computer Vision and Pattern Recognition)