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 "characterizing and predicting repeat food consumption behavior for just in time interventions"):
  Abstract: Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community called MyFitnessPal (MFP), conduct an offline evaluation of various state-of-the-art algorithms in predicting the next-day food consumption, and analyze their performance across different demographic groups and contexts. The experiment results show that algorithms incorporating the exploration-and-exploitation and temporal dynamics are more effective in the next-day recommendation task than most state-of-the-art algorithms.

Includes members:
  Member "goal setting and achievement in activity tracking apps a case study of myfitnesspal":
    also connected through other hyperedges:
      (none)
  Member "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "foodai food image recognition via deep learning for smart food logging")
  Member "is saki delicious the food perception gap on instagram and its relation to health":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction")
  Member "food recommender systems important contributions challenges and future research directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "food recommendation framework existing solutions and challenges")
  Member "fetishizing food in digital age foodporn around the world":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")
  Member "extracting food substitutes from food diary via distributional similarity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on food computing")
  Member "insights from machine learned diet success prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mining social media data for biomedical signals and health related behavior")
  Member "you tweet what you eat studying food consumption through twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "foodai food image recognition via deep learning for smart food logging")

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.IR(Information Retrieval)