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: "global normalization of convolutional neural networks for joint entity and relation classification"
  Abstract: We introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for predicting entity types and relations between entities at the same time. Our experiments show that global normalization outperforms a locally normalized softmax layer on a benchmark dataset.

Connected through hyperedge HE-1 (induced by source paper "end to end named entity recognition and relation extraction using pre trained language models"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "entity relation extraction as multi turn question answering"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "end to end neural relation extraction using deep biaffine attention"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "joint entity recognition and relation extraction as a multi head selection problem"

Connected through hyperedge HE-2 (induced by source paper "type aware convolutional neural networks for slot filling"):
  with members:
    - "cis at tac cold start 2015 neural networks and coreference resolution for slot filling"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "neural architectures for named entity recognition"
    - "comparing convolutional neural networks to traditional models for slot filling"
    - "noise mitigation for neural entity typing and relation extraction"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-3 (induced by source paper "context aware neural based dialog act classification on automatically generated transcriptions"):
  with members:
    - "espnet end to end speech processing toolkit"
    - "neural based context representation learning for dialog act classification"
    - "the microsoft 2017 conversational speech recognition system"
    - "globally normalized transition based neural networks"
    - "neural architectures for named entity recognition"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "the influence of context on dialogue act recognition"
    - "end to end attention based large vocabulary speech recognition"

Connected through hyperedge HE-4 (induced by source paper "comprehend medical a named entity recognition and relationship extraction web service"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "a walk based model on entity graphs for relation extraction"
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "dynamic transfer learning for named entity recognition"
    - "a hybrid deep learning approach for medical relation extraction"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-5 (induced by source paper "end to end neural relation extraction using deep biaffine attention"):
  with members:
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"

Connected through hyperedge HE-6 (induced by source paper "copymtl copy mechanism for joint extraction of entities and relations with multi task learning"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "self normalizing neural networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "neural metric learning for fast end to end relation extraction"):
  with members:
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "effective approaches to attention based neural machine translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-8 (induced by source paper "sub event detection from twitter streams as a sequence labeling problem"):
  with members:
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "an attentive neural architecture for joint segmentation and parsing and its application to real estate ads"
    - "sub story detection in twitter with hierarchical dirichlet processes"
    - "convolutional neural networks for sentence classification"
    - "towards real time summarization of scheduled events from twitter streams"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

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)