human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "dividing and conquering cross modal recipe retrieval from nearest neighbours baselines to sota"):
  Abstract: We propose a novel non-parametric method for cross-modal retrieval which is applied on top of precomputed image and text embeddings. By combining our method with standard approaches for building image and text encoders, trained independently with a self-supervised classification objective, we create a baseline model which outperforms most existing methods on a challenging image-to-recipe task. We also use our method for comparing image and text encoders trained using different modern approaches, thus addressing the issues hindering the developments of novel methods for cross-modal recipe retrieval. We demonstrate how to use the insights from model comparison and extend our baseline model with standard triplet loss that improves SoTA on the Recipe1M dataset by a large margin, while using only precomputed features and with much less complexity than existing methods.

Includes members:
  Member "learning cross modal embeddings with adversarial networks for cooking recipes and food images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cookgan meal image synthesis from ingredients")
  Member "kitchenette predicting and ranking food ingredient pairings using siamese neural network":
    also connected through other hyperedges:
      (none)
  Member "are we really making much progress a worrying analysis of recent neural recommendation approaches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simple and effective graph autoencoders with one hop linear models")
  Member "cross modal retrieval in the cooking context learning semantic text image embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incorporating visual semantics into sentence representations within a grounded space")
  Member "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an artificial intelligence based system to assess nutrient intake for hospitalised patients")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "stacked cross attention for image text matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

Question: Please tell me which class the source paper 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)