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: "modeling complementary products and customer preferences with context knowledge for online recommendation"
  Abstract: Modeling item complementariness and user preferences from purchase data is essential for learning good representations of products and customers, which empowers the modern personalized recommender system for Walmart's e-commerce platform. The intrinsic complementary relationship among products captures the buy-also-buy patterns and provides great sources for recommendations. Product complementary patterns, though often reflected by population purchase behaviors, are not separable from customer-specific bias in purchase data. We propose a unified model with Bayesian network structure that takes account of both factors. In the meantime, we merge the contextual knowledge of both products and customers into their representations. We also use the dual product embeddings to capture the intrinsic properties of complementariness, such as asymmetry. The separating hyperplane theory sheds light on the geometric interpretation of using the additional embedding. We conduct extensive evaluations on our model before final production, and propose a novel ranking criterion based on product and customer embeddings. Our method compares favorably to existing approaches in various offline and online testings, and case studies demonstrate the advantage and usefulness of the dual product embeddings as well as the user embeddings.

Connected through hyperedge HE-1 (induced by source paper "basconv aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network"):
  with members:
    - "personalized bundle list recommendation"
    - "jscn joint spectral convolutional network for cross domain recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "complete the look scene based complementary product recommendation"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"
    - "item2vec neural item embedding for collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "self attention with functional time representation learning"):
  with members:
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "attention is all you need"
    - "time dependent representation for neural event sequence prediction"
    - "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "density estimation using real nvp"

Connected through hyperedge HE-3 (induced by source paper "a large scale deep architecture for personalized grocery basket recommendations"):
  with members:
    - "complementary similarity learning using quadruplet network"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "complete the look scene based complementary product recommendation"
    - "inferring complementary products from baskets and browsing sessions"
    - "billion scale similarity search with gpus"
    - "item2vec neural item embedding for collaborative filtering"
    - "tensorflow a system for large scale machine learning"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"

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.IR(Information Retrieval)