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: "recipe recommendation using ingredient networks"
  Abstract: The recording and sharing of cooking recipes, a human activity dating back thousands of years, naturally became an early and prominent social use of the web. The resulting online recipe collections are repositories of ingredient combinations and cooking methods whose large-scale and variety yield interesting insights about both the fundamentals of cooking and user preferences. At the level of an individual ingredient we measure whether it tends to be essential or can be dropped or added, and whether its quantity can be modified. We also construct two types of networks to capture the relationships between ingredients. The complement network captures which ingredients tend to co-occur frequently, and is composed of two large communities: one savory, the other sweet. The substitute network, derived from user-generated suggestions for modifications, can be decomposed into many communities of functionally equivalent ingredients, and captures users' preference for healthier variants of a recipe. Our experiments reveal that recipe ratings can be well predicted with features derived from combinations of ingredient networks and nutrition information.

Connected through hyperedge HE-1 (induced by source paper "cookgan meal image synthesis from ingredients"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "gilt generating images from long text"
    - "how to make a pizza learning a compositional layer based gan model"
    - "cgans with projection discriminator"
    - "a survey on food computing"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "grab pay and eat semantic food detection for smart restaurants"

Connected through hyperedge HE-2 (induced by source paper "foodai food image recognition via deep learning for smart food logging"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a survey on food computing"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "squeeze and excitation networks"
    - "kissing cuisines exploring worldwide culinary habits on the web"

Connected through hyperedge HE-3 (induced by source paper "clustering in graphs and hypergraphs with categorical edge labels"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "solving linear programs in the current matrix multiplication time"
    - "three hypergraph eigenvector centralities"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "correlation clustering generalized"
    - "spectral sparsification of simplicial complexes for clustering and label propagation"

Connected through hyperedge HE-4 (induced by source paper "the art of food meal image synthesis from ingredients"):
  with members:
    - "cgans with projection discriminator"
    - "a survey on food computing"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "progressive growing of gans for improved quality stability and variation"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-5 (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"
    - "insights from machine learned diet success prediction"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-6 (induced by source paper "food recommendation framework existing solutions and challenges"):
  with members:
    - "a survey on food computing"
    - "hierarchical attention network for visually aware food recommendation"
    - "cross modal health state estimation"
    - "personalized classifier for food image recognition"
    - "food recommender systems important contributions challenges and future research directions"

Connected through hyperedge HE-7 (induced by source paper "eating healthier exploring nutrition information for healthier recipe recommendation"):
  with members:
    - "mpe a mobility pattern embedding model for predicting next locations"
    - "kissing cuisines exploring worldwide culinary habits on the web"
    - "nlpmm a next location predictor with markov modeling"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"

Connected through hyperedge HE-8 (induced by source paper "personalized health aware recipe recommendation an ensemble topic modeling based approach"):
  with members:
    - "food recommender systems important contributions challenges and future research directions"

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.SI(Social and Information Networks)