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: "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
  Abstract: In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images. As the largest publicly available collection of recipe data, Recipe1M+ affords the ability to train high-capacity modelson aligned, multimodal data. Using these data, we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M+ dataset and food and cooking in general. Code, data and models are publicly available.

Connected through hyperedge HE-1 (induced by source paper "an artificial intelligence based system to assess nutrient intake for hospitalised patients"):
  with members:
    - "an artificial intelligence based system for nutrient intake assessment of hospitalised patients"
    - "foodx 251 a dataset for fine grained food classification"
    - "a survey on deep transfer learning"
    - "personalized classifier for food image recognition"
    - "a multi task learning approach for meal assessment"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "food recognition using fusion of classifiers based on cnns"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "procedural reasoning networks for understanding multimodal procedures"):
  with members:
    - "be consistent improving procedural text comprehension using label consistency"
    - "the natural language decathlon multitask learning as question answering"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "neural models for reasoning over multiple mentions using coreference"
    - "relational recurrent neural networks"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "smart home appliances chat with your fridge"):
  with members:
    - "language conditioned graph networks for relational reasoning"
    - "deep modular co attention networks for visual question answering"
    - "deep visual domain adaptation a survey"
    - "compositional attention networks for machine reasoning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "emergence of invariance and disentanglement in deep representations"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised adversarial image inpainting"):
  with members:
    - "misgan learning from incomplete data with generative adversarial networks"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generative image inpainting with contextual attention"
    - "image inpainting using multi scale feature image translation"
    - "deep image prior"

Connected through hyperedge HE-6 (induced by source paper "robot learning and execution of collaborative manipulation plans from youtube videos"):
  with members:
    - "procedure planning in instructional videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "unsupervised learning and segmentation of complex activities from video"
    - "yolov3 an incremental improvement"
    - "functional object oriented network construction expansion"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "towards automatic learning of procedures from web instructional videos"

Connected through hyperedge HE-7 (induced by source paper "self attention and ingredient attention based model for recipe retrieval from image queries"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "a multi task learning approach for meal assessment"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "attention is all you need"
    - "two view 3d reconstruction for food volume estimation"
    - "skip thought vectors"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (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"
    - "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"

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)