human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "product function need recognition via semi supervised attention network"):
  Abstract: Functionality is of utmost importance to customers when they purchase products. However, it is unclear to customers whether a product can really satisfy their needs on functions. Further, missing functions may be intentionally hidden by the manufacturers or the sellers. As a result, a customer needs to spend a fair amount of time before purchasing or just purchase the product on his/her own risk. In this paper, we first identify a novel QA corpus that is dense on product functionality information \footnote{The annotated corpus can be found at \url{this https URL}.}. We then design a neural network called Semi-supervised Attention Network (SAN) to discover product functions from questions. This model leverages unlabeled data as contextual information to perform semi-supervised sequence labeling. We conduct experiments to show that the extracted function have both high coverage and accuracy, compared with a wide spectrum of baselines.

Includes members:
  Member "doc deep open classification of text documents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conditional gaussian distribution learning for open set recognition")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "supervised complementary entity recognition with augmented key value pairs of knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dual attention network for product compatibility and function satisfiability analysis")
  Member "lifelong learning crf for supervised aspect extraction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis")
  Member "mining compatible incompatible entities from question and answering via yes no answer classification using distant label expansion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dual attention network for product compatibility and function satisfiability analysis")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "cer complementary entity recognition via knowledge expansion on large unlabeled product reviews":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "jointly learning explainable rules for recommendation with knowledge graph")
  Member "inferring networks of substitutable and complementary products":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cube2net efficient query specific network construction with data cube organization")

Question: Please tell me which class the source paper 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)