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: "yedda a lightweight collaborative text span annotation tool"
  Abstract: In this paper, we introduce YEDDA, a lightweight but efficient open-source tool for text span annotation. YEDDA provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. YEDDA also gives intelligent recommendations by training a predictive model using the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. YEDDA is developed based on Tkinter and is compatible with all major operating systems.

Connected through hyperedge HE-1 (induced by source paper "a study on agreement in pico span annotations"):
  with members:
    - "a corpus with multi level annotations of patients interventions and outcomes to support language processing for medical literature"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "chinese ner using lattice lstm"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "combining discrete and neural features for sequence labeling"
    - "adversarial multi criteria learning for chinese word segmentation"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "lattice long short term memory for human action recognition"
    - "neural word segmentation with rich pretraining"

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)