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: "named entity recognition for novel types by transfer learning"
  Abstract: In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a method where, given training data in a related domain with similar (but not identical) named entity (NE) types and a small amount of in-domain training data, we use transfer learning to learn a domain-specific NE model. That is, the novelty in the task setup is that we assume not just domain mismatch, but also label mismatch.

Connected through hyperedge HE-1 (induced by source paper "a joint named entity recognizer for heterogeneous tag setsusing a tag hierarchy"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for named entity recognition with neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "de identification of patient notes with recurrent neural networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "transfer learning for sequence labeling using source model and target data"):
  with members:
    - "neuroner an easy to use program for named entity recognition based on neural networks"
    - "progressive neural networks"
    - "how transferable are neural networks in nlp applications"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "adam a method for stochastic optimization"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-3 (induced by source paper "scienceexamcer a high density fine grained science domain corpus for common entity recognition"):
  with members:
    - "ultra fine entity typing"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-5 (induced by source paper "an instance transfer based approach using enhanced recurrent neural network for domain named entity recognition"):
  with members:
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"):
  with members:
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "transfer learning for named entity recognition with neural networks"
    - "unsupervised pretraining for sequence to sequence learning"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural language models"
    - "semi supervised sequence learning"

Connected through hyperedge HE-7 (induced by source paper "zero shot transfer learning for event extraction"):
  with members:
    - "multilingual relation extraction using compositional universal schema"
    - "zero shot learning by convex combination of semantic embeddings"
    - "zero shot learning through cross modal transfer"
    - "efficient estimation of word representations in vector space"

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)