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: "chinesefoodnet a large scale image dataset for chinese food recognition"
  Abstract: In this paper, we introduce a new and challenging large-scale food image dataset called "ChineseFoodNet", which aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In our dataset, images of each food category of our dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. We present our efforts to build this large-scale image dataset, including food category selection, data collection, and data clean and label, in particular how to use machine learning methods to reduce manual labeling work that is an expensive process. We share a detailed benchmark of several state-of-the-art deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose a novel two-step data fusion approach referred as "TastyNet", which combines prediction results from different CNNs with voting method. Our proposed approach achieves top-1 accuracies of 81.43% on the validation set and 81.55% on the test set, respectively. The latest dataset is public available for research and can be achieved at this https URL.

Connected through hyperedge HE-1 (induced by source paper "spatial aware non local attention for fashion landmark detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "non local neural networks"
    - "pose guided person image generation"
    - "attention is all you need"
    - "creating capsule wardrobes from fashion images"
    - "deformable gans for pose based human image generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-2 (induced by source paper "dividing and conquering cross modal recipe retrieval from nearest neighbours baselines to sota"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deep cooking predicting relative food ingredient amounts from images"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "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"
    - "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-4 (induced by source paper "foodx 251 a dataset for fine grained food classification"):
  with members:
    - "combining weakly and webly supervised learning for classifying food images"
    - "webly supervised learning of convolutional networks"
    - "leveraging context to support automated food recognition in restaurants"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-5 (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"

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)