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: "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"
  Abstract: We investigate the incorporation of character-based word representations into a standard CNN-based relation extraction model. We experiment with two common neural architectures, CNN and LSTM, to learn word vector representations from character embeddings. Through a task on the BioCreative-V CDR corpus, extracting relationships between chemicals and diseases, we show that models exploiting the character-based word representations improve on models that do not use this information, obtaining state-of-the-art result relative to previous neural approaches.

Connected through hyperedge HE-1 (induced by source paper "reflex flexible framework for relation extraction in multiple domains"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "biomedical relation extraction with pre trained language representations and minimal task specific architecture"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "assessing bert s syntactic abilities"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "matching the blanks distributional similarity for relation learning"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "improving relation extraction by pre trained language representations"

Connected through hyperedge HE-3 (induced by source paper "connecting the dots document level neural relation extraction with edge oriented graphs"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "document level n ary relation extraction with multiscale representation learning"
    - "a walk based model on entity graphs for relation extraction"
    - "a general framework for information extraction using dynamic span graphs"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-4 (induced by source paper "on the effectiveness of the pooling methods for biomedical relation extraction with deep learning"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "regularizing and optimizing lstm language models"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"
    - "classifying relations by ranking with convolutional neural networks"
    - "a dependency based neural network for relation classification"

Connected through hyperedge HE-5 (induced by source paper "knowledge guided convolutional networks for chemical disease relation extraction"):
  with members:
    - "chemical induced disease relation extraction with dependency information and prior knowledge"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "attending to all mention pairs for full abstract biological relation extraction"
    - "aspect level sentiment classification with deep memory network"
    - "conditional image generation with pixelcnn decoders"
    - "language modeling with gated convolutional networks"
    - "adam a method for stochastic optimization"
    - "a convolutional neural network for modelling sentences"

Connected through hyperedge HE-6 (induced by source paper "a bag of concepts model improves relation extraction in a narrow knowledge domain with limited data"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "deep residual learning for weakly supervised relation extraction"
    - "combining neural networks and log linear models to improve relation extraction"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "combining long short term memory and convolutional neural network for cross sentence n ary relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "n ary relation extraction using graph state lstm"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "classifying relations via long short term memory networks along shortest dependency path"

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)