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: "neural segmental hypergraphs for overlapping mention recognition"
  Abstract: In this work, we propose a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets. We show that our model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference. We also present a theoretical analysis to formally assess how our representation is better than alternative representations reported in the literature in terms of representational power. Coupled with neural networks for feature learning, our model achieves the state-of-the-art performance in three benchmark datasets annotated with overlapping mentions.

Connected through hyperedge HE-1 (induced by source paper "a unified mrc framework for named entity recognition"):
  with members:
    - "entity relation extraction as multi turn question answering"
    - "glyce glyph vectors for chinese character representations"
    - "nested named entity recognition via second best sequence learning and decoding"
    - "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"
    - "a general framework for information extraction using dynamic span graphs"
    - "chinese ner using lattice lstm"

Connected through hyperedge HE-2 (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"
    - "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 relation extraction via reading comprehension"
    - "dcn mixed objective and deep residual coattention for question answering"

Connected through hyperedge HE-3 (induced by source paper "a general framework for information extraction using dynamic span graphs"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "higher order coreference resolution with coarse to fine inference"

Connected through hyperedge HE-4 (induced by source paper "multi grained named entity recognition"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "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"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"

Connected through hyperedge HE-5 (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"
    - "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"
    - "learning to recognize discontiguous entities"

Connected through hyperedge HE-6 (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"
    - "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"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "sequence to nuggets nested entity mention detection via anchor region networks"):
  with members:
    - "ultra fine entity typing"
    - "a neural transition based model for nested 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"

Connected through hyperedge HE-8 (induced by source paper "neural architectures for nested ner through linearization"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"
    - "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"

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)