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: "fruit recognition from images using deep learning"
  Abstract: In this paper we introduce a new, high-quality, dataset of images containing fruits. We also present the results of some numerical experiment for training a neural network to detect fruits. We discuss the reason why we chose to use fruits in this project by proposing a few applications that could use this kind of neural network.

Connected through hyperedge HE-1 (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"
    - "incremental few shot learning with attention attractor networks"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-2 (induced by source paper "a hierarchical grocery store image dataset with visual and semantic labels"):
  with members:
    - "advances in variational inference"
    - "deep variational canonical correlation analysis"
    - "generative models of visually grounded imagination"
    - "densely connected convolutional networks"
    - "inter battery topic representation learning"
    - "deep fruit detection in orchards"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-3 (induced by source paper "inspecting adversarial examples using the fisher information"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "the adversarial attack and detection under the fisher information metric"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "safetynet detecting and rejecting adversarial examples robustly"

Connected through hyperedge HE-4 (induced by source paper "a comprehensive application oriented study of catastrophic forgetting in dnns"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "keep and learn continual learning by constraining the latent space for knowledge preservation in neural networks"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "continual learning with deep generative replay"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "measuring catastrophic forgetting in neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "dct compcnn a novel image classification network using jpeg compressed dct coefficients"):
  with members:
    - "a feature learning and object recognition framework for underwater fish images"

Connected through hyperedge HE-6 (induced by source paper "a survey on food computing"):
  with members:
    - "food recommendation framework existing solutions and challenges"
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "recognition of multiple food items in a single photo for use in a buffet style restaurant"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "scaling egocentric vision the epic kitchens dataset"
    - "rice classification using spatio spectral deep convolutional neural network"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "single view food portion estimation learning image to energy mappings using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "understanding convolutional neural networks with information theory an initial exploration"):
  with members:
    - "adaptive estimators show information compression in deep neural networks"
    - "understanding individual neuron importance using information theory"
    - "understanding autoencoders with information theoretic concepts"
    - "estimating information flow in neural networks"
    - "simple stopping criteria for information theoretic feature selection"
    - "emergence of invariance and disentanglement in deep representations"
    - "thinet a filter level pruning method for deep neural network compression"
    - "opening the black box of deep neural networks via information"

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)