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: "cer complementary entity recognition via knowledge expansion on large unlabeled product reviews"
  Abstract: Product reviews contain a lot of useful information about product features and customer opinions. One important product feature is the complementary entity (products) that may potentially work together with the reviewed product. Knowing complementary entities of the reviewed product is very important because customers want to buy compatible products and avoid incompatible ones. In this paper, we address the problem of Complementary Entity Recognition (CER). Since no existing method can solve this problem, we first propose a novel unsupervised method to utilize syntactic dependency paths to recognize complementary entities. Then we expand category-level domain knowledge about complementary entities using only a few general seed verbs on a large amount of unlabeled reviews. The domain knowledge helps the unsupervised method to adapt to different products and greatly improves the precision of the CER task. The advantage of the proposed method is that it does not require any labeled data for training. We conducted experiments on 7 popular products with about 1200 reviews in total to demonstrate that the proposed approach is effective.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "dual attention network for product compatibility and function satisfiability analysis"):
  with members:
    - "lstm a search space odyssey"
    - "supervised complementary entity recognition with augmented key value pairs of knowledge"
    - "product function need recognition via semi supervised attention network"
    - "dataset and neural recurrent sequence labeling model for open domain factoid question answering"
    - "squad 100 000 questions for machine comprehension of text"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "mining compatible incompatible entities from question and answering via yes no answer classification using distant label expansion"

Connected through hyperedge HE-3 (induced by source paper "product function need recognition via semi supervised attention network"):
  with members:
    - "doc deep open classification of text documents"
    - "lstm a search space odyssey"
    - "supervised complementary entity recognition with augmented key value pairs of knowledge"
    - "lifelong learning crf for supervised aspect extraction"
    - "mining compatible incompatible entities from question and answering via yes no answer classification using distant label expansion"
    - "neural architectures for named entity recognition"
    - "inferring networks of substitutable and complementary products"
    - "addressing complex and subjective product related queries with customer reviews"

Connected through hyperedge HE-4 (induced by source paper "mining compatible incompatible entities from question and answering via yes no answer classification using distant label expansion"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "addressing complex and subjective product related queries with customer reviews"

Connected through hyperedge HE-5 (induced by source paper "supervised opinion aspect extraction by exploiting past extraction results"):
  with members:
    - "mining compatible incompatible entities from question and answering via yes no answer classification using distant label expansion"

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.CL(Computation and Language)