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 "deep cooking predicting relative food ingredient amounts from images"):
  Abstract: In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction-based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database integrative data pre-processing, to solve the problem. Experiments on a dataset of recipes collected from the Internet show the models generate encouraging experimental results.

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 "single view food portion estimation learning image to energy mappings using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on food computing")
  Member "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an artificial intelligence based system to assess nutrient intake for hospitalised patients")
  Member "inverse cooking recipe generation from food images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recipegpt generative pre training based cooking recipe generation and evaluation system")
  Member "chinesefoodnet a large scale image dataset for chinese food recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatial aware non local attention for fashion landmark detection")
  Member "two view 3d reconstruction for food volume estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an artificial intelligence based system to assess nutrient intake for hospitalised patients")
  Member "computer vision based food calorie estimation dataset method and experiment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on food computing")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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.LG(Machine Learning)