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: "coordination annotation extension in the penn tree bank"
  Abstract: Coordination is an important and common syntactic construction which is not handled well by state of the art parsers. Coordinations in the Penn Treebank are missing internal structure in many cases, do not include explicit marking of the conjuncts and contain various errors and inconsistencies. In this work, we initiated manual annotation process for solving these issues. We identify the different elements in a coordination phrase and label each element with its function. We add phrase boundaries when these are missing, unify inconsistencies, and fix errors. The outcome is an extension of the PTB that includes consistent and detailed structures for coordinations. We make the coordination annotation publicly available, in hope that they will facilitate further research into coordination disambiguation.

Connected through hyperedge HE-1 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "why self attention a targeted evaluation of neural machine translation architectures"

Connected through hyperedge HE-2 (induced by source paper "representation of constituents in neural language models coordination phrase as a case study"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "what do rnn language models learn about filler gap dependencies"
    - "rnns as psycholinguistic subjects syntactic state and grammatical dependency"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-3 (induced by source paper "nne a dataset for nested named entity recognition in english newswire"):
  with members:
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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)