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: "insights from machine learned diet success prediction"
  Abstract: To support people trying to lose weight and stay healthy, more and more fitness apps have sprung up including the ability to track both calories intake and expenditure. Users of such apps are part of a wider ``quantified self'' movement and many opt-in to publicly share their logged data. In this paper, we use public food diaries of more than 4,000 long-term active MyFitnessPal users to study the characteristics of a (un-)successful diet. Concretely, we train a machine learning model to predict repeatedly being over or under self-set daily calories goals and then look at which features contribute to the model's prediction. Our findings include both expected results, such as the token ``mcdonalds'' or the category ``dessert'' being indicative for being over the calories goal, but also less obvious ones such as the difference between pork and poultry concerning dieting success, or the use of the ``quick added calories'' functionality being indicative of over-shooting calorie-wise. This study also hints at the feasibility of using such data for more in-depth data mining, e.g., looking at the interaction between consumed foods such as mixing protein- and carbohydrate-rich foods. To the best of our knowledge, this is the first systematic study of public food diaries.

Connected through hyperedge HE-1 (induced by source paper "mining social media data for biomedical signals and health related behavior"):
  with members:
    - "singular value decomposition and neural networks"
    - "anatomy of an online misinformation network"
    - "human sexual cycles are driven by culture and match collective moods"
    - "limited individual attention and online virality of low quality information"
    - "quantified self meets social media sharing of weight updates on twitter"
    - "the rise of social bots"
    - "learning deep structure preserving image text embeddings"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"

Connected through hyperedge HE-2 (induced by source paper "characterizing and predicting repeat food consumption behavior for just in time interventions"):
  with members:
    - "goal setting and achievement in activity tracking apps a case study of myfitnesspal"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "food recommender systems important contributions challenges and future research directions"
    - "fetishizing food in digital age foodporn around the world"
    - "extracting food substitutes from food diary via distributional similarity"
    - "you tweet what you eat studying food consumption through twitter"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-3 (induced by source paper "hierarchical attention network for visually aware food recommendation"):
  with members:
    - "discrete factorization machines for fast feature based recommendation"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-4 (induced by source paper "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"):
  with members:
    - "persistent sharing of fitness app status on twitter"
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-5 (induced by source paper "quantified self meets social media sharing of weight updates on twitter"):
  with members:
    - "crowdsourcing health labels inferring body weight from profile pictures"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-6 (induced by source paper "extracting food substitutes from food diary via distributional similarity"):
  with members:
    - "recipe recommendation using ingredient networks"
    - "from frequency to meaning vector space models of semantics"

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.HC(Human-Computer Interaction)