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: "a neural transition based model for nested mention recognition"
  Abstract: It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We first map a sentence with nested mentions to a designated forest where each mention corresponds to a constituent of the forest. Our shift-reduce based system then learns to construct the forest structure in a bottom-up manner through an action sequence whose maximal length is guaranteed to be three times of the sentence length. Based on Stack-LSTM which is employed to efficiently and effectively represent the states of the system in a continuous space, our system is further incorporated with a character-based component to capture letter-level patterns. Our model achieves the state-of-the-art results on ACE datasets, showing its effectiveness in detecting nested mentions.

Connected through hyperedge HE-1 (induced by source paper "query based named entity recognition"):
  with members:
    - "glyce glyph vectors for chinese character representations"
    - "chinese ner using lattice lstm"
    - "the natural language decathlon multitask learning as question answering"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot relation extraction via reading comprehension"
    - "dcn mixed objective and deep residual coattention for question answering"

Connected through hyperedge HE-2 (induced by source paper "multi grained named entity recognition"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot user intent detection via capsule neural networks"
    - "end to end neural coreference resolution"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"

Connected through hyperedge HE-3 (induced by source paper "combining spans into entities a neural two stage approach for recognizing discontiguous entities"):
  with members:
    - "sequence to nuggets nested entity mention detection via anchor region networks"
    - "merge and label a novel neural network architecture for nested ner"
    - "neural architectures for nested ner through linearization"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "sequence to sequence learning as beam search optimization"
    - "learning to recognize discontiguous entities"

Connected through hyperedge HE-4 (induced by source paper "sequence to nuggets nested entity mention detection via anchor region networks"):
  with members:
    - "ultra fine entity typing"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "nugget proposal networks for chinese event detection"
    - "attention is all you need"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "squad 100 000 questions for machine comprehension of text"
    - "multi perspective context matching for machine comprehension"
    - "machine comprehension using match lstm and answer pointer"

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)