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: "attribute extraction from product titles in ecommerce"
  Abstract: This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Structured Perceptron with a curated normalization scheme produces an effective system for the task of extracting product attribute values from titles. To keep the discussion concrete, we will illustrate the mechanics of the system from the point of view of a particular attribute - brand. We also discuss the importance of an attribute extraction system in the context of retail websites with large product catalogs, compare our approach to other potential approaches to this problem and end the paper with a discussion of the performance of our system for extracting attributes.

Connected through hyperedge HE-1 (induced by source paper "deep recurrent neural networks for product attribute extraction in ecommerce"):
  with members:
    - "we used neural networks to detect clickbaits you won t believe what happened next"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "query understanding via entity attribute identification"):
  with members:
    - "question answering on freebase via relation extraction and textual evidence"
    - "distributed representations of words and phrases and their compositionality"
    - "tagme on the fly annotation of short text fragments by wikipedia entities"

Connected through hyperedge HE-3 (induced by source paper "multimodal attribute extraction"):
  with members:
    - "bottom up and top down attention for image captioning and visual question answering"
    - "squad 100 000 questions for machine comprehension of text"
    - "vqa visual question answering"
    - "rethinking the inception architecture for computer vision"
    - "combining language and vision with a multimodal skip gram model"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"

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)