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: "inferring networks of substitutable and complementary products"
  Abstract: In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. #R##N#Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.

Connected through hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "relation learning on social networks with multi modal graph edge variational autoencoders"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "place deduplication with embeddings"
    - "query specific knowledge summarization with entity evolutionary networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"

Connected through hyperedge HE-2 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "a multi task learning approach for improving product title compression with user search log data"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-3 (induced by source paper "basconv aggregating heterogeneous interactions for basket recommendation with graph convolutional neural network"):
  with members:
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep neural review text interaction for recommendation systems"):
  with members:
    - "neural rating regression with abstractive tips generation for recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "transnets learning to transform for recommendation"
    - "text matching as image recognition"
    - "image based recommendations on styles and substitutes"
    - "learning distributed representations from reviews for collaborative filtering"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-5 (induced by source paper "explainable product search with a dynamic relation embedding model"):
  with members:
    - "conversational product search based on negative feedback"
    - "leverage implicit feedback for context aware product search"
    - "a zero attention model for personalized product search"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "learning a deep listwise context model for ranking refinement"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "learning over knowledge base embeddings for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"

Connected through hyperedge HE-6 (induced by source paper "tigs an inference algorithm for text infilling with gradient search"):
  with members:
    - "text infilling"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "detecting egregious responses in neural sequence to sequence models"
    - "a multi modal chinese poetry generation model"
    - "bidirectional beam search forward backward inference in neural sequence models for fill in the blank image captioning"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "robust learning from untrusted sources"):
  with members:
    - "byzantine stochastic gradient descent"
    - "algorithms and theory for multiple source adaptation"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "improving the generalization of adversarial training with domain adaptation"
    - "mitigating sybils in federated learning poisoning"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "on fundamental limits of robust learning"

Connected through hyperedge HE-8 (induced by source paper "jointly learning explainable rules for recommendation with knowledge graph"):
  with members:
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "deep reinforcement learning for page wise recommendations"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "outer product based neural collaborative filtering"
    - "explainable recommendation a survey and new perspectives"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"

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)