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: "learning to recognize discontiguous entities"
  Abstract: This paper focuses on the study of recognizing discontiguous entities. Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another. To compare with existing approaches, we first formally introduce the notion of model ambiguity, which defines the difficulty level of interpreting the outputs of a model, and then formally analyze the theoretical advantages of our model over previous existing approaches based on linear-chain CRFs. Our empirical results also show that our model is able to achieve significantly better results when evaluated on standard data with many discontiguous entities.

Connected through hyperedge HE-1 (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"
    - "a neural transition based model for nested 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"

Connected through hyperedge HE-2 (induced by source paper "a search based neural model for biomedical nested and overlapping event detection"):
  with members:
    - "on the convergence of adam and beyond"
    - "globally normalized transition based neural networks"
    - "transition based dependency parsing with stack long short term memory"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-3 (induced by source paper "neural segmental hypergraphs for overlapping mention recognition"):
  with members:
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "segmental recurrent neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a neural transition based model for nested mention recognition"):
  with members:
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "transition based dependency parsing with stack long short term memory"
    - "named entity recognition with bidirectional lstm cnns"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "efficient dependency guided named entity recognition"):
  with members:
    - "weak semi markov crfs for noun phrase chunking in informal text"
    - "learning dependency based compositional semantics"

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)