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: "market basket prediction using user centric temporal annotated recurring sequences"
  Abstract: Nowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers' purchase behavior, and that TBP outperforms the state-of-the-art competitors.

Connected through hyperedge HE-1 (induced by source paper "a long short demands aware model for next item recommendation"):
  with members:
    - "sequence aware recommender systems"
    - "neural tensor factorization"
    - "neural collaborative filtering"
    - "translation based recommendation"
    - "scalable demand aware recommendation"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "large scale and high resolution analysis of food purchases and health outcomes"):
  with members:
    - "face to bmi using computer vision to infer body mass index on social media"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "fetishizing food in digital age foodporn around the world"
    - "kissing cuisines exploring worldwide culinary habits on the web"
    - "crowdsourcing health labels inferring body weight from profile pictures"
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-3 (induced by source paper "personalized purchase prediction of market baskets with wasserstein based sequence matching"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "clustering for point pattern data"
    - "neural collaborative filtering"
    - "session based recommendations with recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "pre training of context aware item representation for next basket recommendation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised multitask learning for sequence labeling"

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.DB(Databases)