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: "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
  Abstract: Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle. An important task under the food-computing umbrella is retrieval, which is particularly helpful for health related applications, where we are interested in retrieving important information about food (e.g., ingredients, nutrition, etc.). In this paper, we investigate an open research task of cross-modal retrieval between cooking recipes and food images, and propose a novel framework Adversarial Cross-Modal Embedding (ACME) to resolve the cross-modal retrieval task in food domains. Specifically, the goal is to learn a common embedding feature space between the two modalities, in which our approach consists of several novel ideas: (i) learning by using a new triplet loss scheme together with an effective sampling strategy, (ii) imposing modality alignment using an adversarial learning strategy, and (iii) imposing cross-modal translation consistency such that the embedding of one modality is able to recover some important information of corresponding instances in the other modality. ACME achieves the state-of-the-art performance on the benchmark Recipe1M dataset, validating the efficacy of the proposed technique.

Connected through hyperedge HE-1 (induced by source paper "cookgan meal image synthesis from ingredients"):
  with members:
    - "gilt generating images from long text"
    - "how to make a pizza learning a compositional layer based gan model"
    - "cgans with projection discriminator"
    - "a survey on food computing"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "safe similarity aware multi modal fake news detection"):
  with members:
    - "fake news early detection a theory driven model"
    - "learning hierarchical discourse level structure for fake news detection"
    - "different spirals of sameness a study of content sharing in mainstream and alternative media"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake news a survey of research detection methods and opportunities"
    - "ti cnn convolutional neural networks for fake news detection"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"

Connected through hyperedge HE-3 (induced by source paper "cross modal food retrieval learning a joint embedding of food images and recipes with semantic consistency and attention mechanism"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "in defense of the triplet loss for person re identification"
    - "attention is all you need"
    - "skip thought vectors"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "dividing and conquering cross modal recipe retrieval from nearest neighbours baselines to sota"):
  with members:
    - "kitchenette predicting and ranking food ingredient pairings using siamese neural network"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "arcface additive angular margin loss for deep face recognition"
    - "stacked cross attention for image text matching"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "chinesefoodnet a large scale image dataset for chinese food recognition"

Connected through hyperedge HE-5 (induced by source paper "deep cooking predicting relative food ingredient amounts from images"):
  with members:
    - "single view food portion estimation learning image to energy mappings using generative adversarial networks"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "inverse cooking recipe generation from food images"
    - "chinesefoodnet a large scale image dataset for chinese food recognition"
    - "two view 3d reconstruction for food volume estimation"
    - "computer vision based food calorie estimation dataset method and experiment"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "estimating glycemic impact of cooking recipes via online crowdsourcing and machine learning"):
  with members:
    - "a neural network system for transformation of regional cuisine style"
    - "enriching word vectors with subword information"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "a survey on food computing"):
  with members:
    - "food recommendation framework existing solutions and challenges"
    - "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"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"

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)