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: "mining compatible incompatible entities from question and answering via yes no answer classification using distant label expansion"
  Abstract: Product Community Question Answering (PCQA) provides useful information about products and their features (aspects) that may not be well addressed by product descriptions and reviews. We observe that a product's compatibility issues with other products are frequently discussed in PCQA and such issues are more frequently addressed in accessories, i.e., via a yes/no question "Does this mouse work with windows 10?". In this paper, we address the problem of extracting compatible and incompatible products from yes/no questions in PCQA. This problem can naturally have a two-stage framework: first, we perform Complementary Entity (product) Recognition (CER) on yes/no questions; second, we identify the polarities of yes/no answers to assign the complementary entities a compatibility label (compatible, incompatible or unknown). We leverage an existing unsupervised method for the first stage and a 3-class classifier by combining a distant PU-learning method (learning from positive and unlabeled examples) together with a binary classifier for the second stage. The benefit of using distant PU-learning is that it can help to expand more implicit yes/no answers without using any human annotated data. We conduct experiments on 4 products to show that the proposed method is effective.

Connected through hyperedge HE-1 (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"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-2 (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"
    - "neural architectures for named entity recognition"
    - "cer complementary entity recognition via knowledge expansion on large unlabeled product reviews"
    - "inferring networks of substitutable and complementary products"
    - "addressing complex and subjective product related queries with customer reviews"

Connected through hyperedge HE-3 (induced by source paper "supervised complementary entity recognition with augmented key value pairs of knowledge"):
  with members:
    - "lifelong learning crf for supervised aspect extraction"
    - "supervised opinion aspect extraction by exploiting past extraction results"
    - "inferring networks of substitutable and complementary products"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-4 (induced by source paper "supervised opinion aspect extraction by exploiting past extraction results"):
  with members:
    - "cer complementary entity recognition via knowledge expansion on large unlabeled product reviews"

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)