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: "labeling gaps between words recognizing overlapping mentions with mention separators"
  Abstract: In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.

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

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "learning word embeddings with domain awareness"):
  with members:
    - "parser training with heterogeneous treebanks"
    - "factors influencing the surprising instability of word embeddings"
    - "on the dimensionality of word embedding"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "integrating distributional lexical contrast into word embeddings for antonym synonym distinction"
    - "bag of tricks for efficient text classification"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "merge and label a novel neural network architecture for nested ner"):
  with members:
    - "a general framework for information extraction using dynamic span graphs"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "self normalizing neural networks"
    - "attention is all you need"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (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"
    - "a neural transition based model for nested mention recognition"
    - "nugget proposal networks for chinese event detection"
    - "attention is all you need"
    - "squad 100 000 questions for machine comprehension of text"
    - "multi perspective context matching for machine comprehension"
    - "machine comprehension using match lstm and answer pointer"

Connected through hyperedge HE-6 (induced by source paper "neural architectures for nested ner through linearization"):
  with members:
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "shift reduce constituent parsing with neural lookahead features"
    - "multilingual language processing from bytes"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "nested named entity recognition via second best sequence learning and decoding"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "neural architectures for named entity recognition"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-8 (induced by source paper "nne a dataset for nested named entity recognition in english newswire"):
  with members:
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "coordination annotation extension in the penn tree bank"
    - "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)