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: "extending a parser to distant domains using a few dozen partially annotated examples"
  Abstract: We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Jour- nal alone that achieves over 90% F1 on the Brown corpus. For more syntactically dis- tant domains, we provide a simple way to adapt a parser using only dozens of partial annotations. For instance, we increase the percentage of error-free geometry-domain parses in a held-out set from 45% to 73% using approximately five dozen training examples. In the process, we demon- strate a new state-of-the-art single model result on the Wall Street Journal test set of 94.3%. This is an absolute increase of 1.7% over the previous state-of-the-art of 92.6%.

Connected through hyperedge HE-1 (induced by source paper "parsing early modern english for linguistic search"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "scibert a pretrained language model for scientific text"
    - "probing biomedical embeddings from language models"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence tagging with bidirectional language models"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "cross domain generalization of neural constituency parsers"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"

Connected through hyperedge HE-3 (induced by source paper "scispacy fast and robust models for biomedical natural language processing"):
  with members:
    - "structural scaffolds for citation intent classification in scientific publications"
    - "from pos tagging to dependency parsing for biomedical event extraction"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "construction of the literature graph in semantic scholar"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "effective use of bidirectional language modeling for transfer learning in biomedical named entity recognition"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-4 (induced by source paper "automatic generation of high quality ccgbanks for parser domain adaptation"):
  with members:
    - "deep contextualized word representations"
    - "langpro natural language theorem prover"
    - "a ccg parsing with a supertag and dependency factored model"
    - "deep biaffine attention for neural dependency parsing"
    - "global neural ccg parsing with optimality guarantees"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "improved semantic representations from tree structured long short term memory networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "dissecting contextual word embeddings architecture and representation"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "deep rnns encode soft hierarchical syntax"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "constituency parsing with a self attentive encoder"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-6 (induced by source paper "multilingual constituency parsing with self attention and pre training"):
  with members:
    - "two local models for neural constituent parsing"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "constituency parsing with a self attentive encoder"
    - "policy gradient as a proxy for dynamic oracles in constituency parsing"
    - "in order transition based constituent parsing"
    - "improving neural parsing by disentangling model combination and reranking effects"

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)