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: "supervised complementary entity recognition with augmented key value pairs of knowledge"
  Abstract: Extracting opinion targets is an important task in sentiment analysis on product reviews and complementary entities (products) are one important type of opinion targets that may work together with the reviewed product. In this paper, we address the problem of Complementary Entity Recognition (CER) as a supervised sequence labeling with the capability of expanding domain knowledge as key-value pairs from unlabeled reviews, by automatically learning and enhancing knowledge-based features. We use Conditional Random Field (CRF) as the base learner and augment CRF with knowledge-based features (called the Knowledge-based CRF or KCRF for short). We conduct experiments to show that KCRF effectively improves the performance of supervised CER task.

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"
    - "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"
    - "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"
    - "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"
    - "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"

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)