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: "hierarchy based image embeddings for semantic image retrieval"
  Abstract: Deep neural networks trained for classification have been found to learn powerful image representations, which are also often used for other tasks such as comparing images w.r.t. their visual similarity. However, visual similarity does not imply semantic similarity. In order to learn semantically discriminative features, we propose to map images onto class embeddings whose pair-wise dot products correspond to a measure of semantic similarity between classes. Such an embedding does not only improve image retrieval results, but could also facilitate integrating semantics for other tasks, e.g., novelty detection or few-shot learning. We introduce a deterministic algorithm for computing the class centroids directly based on prior world-knowledge encoded in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds, and ImageNet show that our learned semantic image embeddings improve the semantic consistency of image retrieval results by a large margin.

Connected through hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers"):
  with members:
    - "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"
    - "lshtc a benchmark for large scale text classification"

Connected through hyperedge HE-2 (induced by source paper "deep learning on small datasets without pre training using cosine loss"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "tafe net task aware feature embeddings for low shot learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bag of tricks for image classification with convolutional neural networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "mixed dimension embeddings with application to memory efficient recommendation systems"):
  with members:
    - "on the dimensionality of embeddings for sparse features and data"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "saec similarity aware embedding compression in recommendation systems"
    - "practice on long sequential user behavior modeling for click through rate prediction"
    - "tensorized embedding layers for efficient model compression"
    - "block based singular value decomposition approach to matrix factorization for recommender systems"
    - "on the difficulty of evaluating baselines a study on recommender systems"

Connected through hyperedge HE-4 (induced by source paper "making better mistakes leveraging class hierarchies with deep networks"):
  with members:
    - "integrating domain knowledge using hierarchies to improve deep classifiers"
    - "when does label smoothing help"
    - "class balanced loss based on effective number of samples"
    - "theoretically principled trade off between robustness and accuracy"
    - "on the convergence of adam and beyond"
    - "do convolutional neural networks learn class hierarchy"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "huse hierarchical universal semantic embeddings"):
  with members:
    - "adversarial representation learning for text to image matching"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "vse improving visual semantic embeddings with hard negatives"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"
    - "learning structured semantic embeddings for visual recognition"

Connected through hyperedge HE-6 (induced by source paper "shrewd semantic hierarchy based relational embeddings for weakly supervised deep hashing"):
  with members:
    - "semantic hierarchy preserving deep hashing for large scale image retrieval"
    - "dual asymmetric deep hashing learning"
    - "mean local group average precision mlgap a new performance metric for hashing based retrieval"
    - "label embedding network learning label representation for soft training of deep networks"
    - "unsupervised semantic deep hashing"
    - "hashnet deep learning to hash by continuation"
    - "supervised deep hashing for hierarchical labeled data"
    - "a survey on learning to hash"

Connected through hyperedge HE-7 (induced by source paper "not just a matter of semantics the relationship between visual similarity and semantic similarity"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a survey on deep learning in medical image analysis"
    - "semantic similarity from natural language and ontology analysis"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

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)