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: "deep style match for complementary recommendation"
  Abstract: Humans develop a common sense of style compatibility between items based on their attributes. We seek to automatically answer questions like "Does this shirt go well with that pair of jeans?" In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper. The basic assumption of our approach is that most of the important attributes for a product in an online store are included in its title description. Therefore it is feasible to learn style compatibility from these descriptions. We design a Siamese Convolutional Neural Network architecture and feed it with title pairs of items, which are either compatible or incompatible. Those pairs will be mapped from the original space of symbolic words into some embedded style space. Our approach takes only words as the input with few preprocessing and there is no laborious and expensive feature engineering.

Connected through hyperedge HE-1 (induced by source paper "session based complementary fashion recommendations"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "inferring complementary products from baskets and browsing sessions"
    - "fashion dna merging content and sales data for recommendation and article mapping"
    - "tensorflow a system for large scale machine learning"
    - "session based recommendations with recurrent neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning and transferring ids representation in e commerce"):
  with members:
    - "customer lifetime value prediction using embeddings"
    - "item2vec neural item embedding for collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "tensorflow a system for large scale machine learning"
    - "image based recommendations on styles and substitutes"
    - "line large scale information network embedding"

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.AI(Artificial Intelligence)