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: "embedding label structures for fine grained feature representation"
  Abstract: Recent algorithms in convolutional neural networks (CNN) considerably advance the fine-grained image classification, which aims to differentiate subtle differences among subordinate classes. However, previous studies have rarely focused on learning a fined-grained and structured feature representation that is able to locate similar images at different levels of relevance, e.g., discovering cars from the same make or the same model, both of which require high precision. In this paper, we propose two main contributions to tackle this problem. 1) A multi-task learning framework is designed to effectively learn fine-grained feature representations by jointly optimizing both classification and similarity constraints. 2) To model the multi-level relevance, label structures such as hierarchy or shared attributes are seamlessly embedded into the framework by generalizing the triplet loss. Extensive and thorough experiments have been conducted on three fine-grained datasets, i.e., the Stanford car, the car-333, and the food datasets, which contain either hierarchical labels or shared attributes. Our proposed method has achieved very competitive performance, i.e., among state-of-the-art classification accuracy. More importantly, it significantly outperforms previous fine-grained feature representations for image retrieval at different levels of relevance.

Connected through hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "not just a matter of semantics the relationship between visual similarity and semantic similarity"
    - "do convolutional neural networks learn class hierarchy"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "the inaturalist species classification and detection dataset"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "few shot learning for domain specfic fine grained image classfication"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few shot learning with metric agnostic conditional embeddings"
    - "discriminative learning of latent features for zero shot recognition"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "multi scale location aware kernel representation for object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "git loss for deep face recognition"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-3 (induced by source paper "cross x learning for fine grained visual categorization"):
  with members:
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "sampling matters in deep embedding learning"
    - "deep layer aggregation"
    - "dynamic computational time for visual attention"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-4 (induced by source paper "deep metric learning with alternating projections onto feasible sets"):
  with members:
    - "ranked list loss for deep metric learning"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "hardness aware deep metric learning"
    - "divide and conquer the embedding space for metric learning"
    - "a theoretically sound upper bound on the triplet loss for improving the efficiency of deep distance metric learning"
    - "deep randomized ensembles for metric learning"
    - "deep metric learning with hierarchical triplet loss"

Connected through hyperedge HE-5 (induced by source paper "the group loss for deep metric learning"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "learning with average precision training image retrieval with a listwise loss"
    - "relational knowledge distillation"
    - "divide and conquer the embedding space for metric learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "classification is a strong baseline for deep metric learning"

Connected through hyperedge HE-6 (induced by source paper "domain invariant hierarchical embedding for grocery products recognition"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "product recognition in store shelves as a sub graph isomorphism problem"
    - "prototypical networks for few shot learning"
    - "adversarial discriminative domain adaptation"
    - "learning spread out local feature descriptors"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "semantic granularity metric learning for visual search"):
  with members:
    - "generalization in metric learning should the embedding layer be the embedding layer"
    - "deep randomized ensembles for metric learning"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "deep metric learning with hierarchical triplet loss"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "in defense of the triplet loss for person re identification"
    - "local similarity aware deep feature embedding"
    - "hard aware deeply cascaded embedding"

Connected through hyperedge HE-8 (induced by source paper "deep metric learning with density adaptivity"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "learning spread out local feature descriptors"
    - "understanding deep learning requires rethinking generalization"
    - "deep metric learning via facility location"
    - "local similarity aware deep feature embedding"
    - "learning deep embeddings with histogram loss"
    - "hard aware deeply cascaded embedding"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

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)