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 "fully automatic semantic segmentation for food intake tracking in long term care homes"):
  Abstract: Malnutrition impacts quality of life and places annually-recurring burden on the health care system. Half of older adults are at risk for malnutrition in long-term care (LTC). Monitoring and measuring nutritional intake is paramount yet involves time-consuming and subjective visual assessment, limiting current methods' reliability. The opportunity for automatic image-based estimation exists. Some progress outside LTC has been made (e.g., calories consumed, food classification), however, these methods have not been implemented in LTC, potentially due to a lack of ability to independently evaluate automatic segmentation methods within the intake estimation pipeline. Here, we propose and evaluate a novel fully-automatic semantic segmentation method for pixel-level classification of food on a plate using a deep convolutional neural network (DCNN). The macroarchitecture of the DCNN is a multi-scale encoder-decoder food network (EDFN) architecture comprising a residual encoder microarchitecture, a pyramid scene parsing decoder microarchitecture, and a specialized per-pixel food/no-food classification layer. The network was trained and validated on the pre-labelled UNIMIB 2016 food dataset (1027 tray images, 73 categories), and tested on our novel LTC plate dataset (390 plate images, 9 categories). Our fully-automatic segmentation method attained similar intersection over union to the semi-automatic graph cuts (91.2% vs. 93.7%). Advantages of our proposed system include: testing on a novel dataset, decoupled error analysis, no user-initiated annotations, with similar segmentation accuracy and enhanced reliability in terms of types of segmentation errors. This may address several short-comings currently limiting utility of automated food intake tracking in time-constrained LTC and hospital settings.

Includes members:
  Member "auto deeplab hierarchical neural architecture search for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "grab pay and eat semantic food detection for smart restaurants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an artificial intelligence based system to assess nutrient intake for hospitalised patients")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "pyramid scene parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)