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: "sentence state lstm for text representation"
  Abstract: Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investigate an alternative LSTM structure for encoding text, which consists of a parallel state for each word. Recurrent steps are used to perform local and global information exchange between words simultaneously, rather than incremental reading of a sequence of words. Results on various classification and sequence labelling benchmarks show that the proposed model has strong representation power, giving highly competitive performances compared to stacked BiLSTM models with similar parameter numbers.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"

Connected through hyperedge HE-2 (induced by source paper "adaptive fractional order graph neural network"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "cross sentence n ary relation extraction with graph lstms"
    - "attention is all you need"
    - "robust spatial filtering with graph convolutional neural networks"
    - "learning combinatorial optimization algorithms over graphs"
    - "gated graph sequence neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "agl a scalable system for industrial purpose graph machine learning"):
  with members:
    - "dsslp a distributed framework for semi supervised link prediction"
    - "heterogeneous graph neural networks for malicious account detection"
    - "fast graph representation learning with pytorch geometric"
    - "pytorch biggraph a large scale graph embedding system"
    - "graph convolutional neural networks for web scale recommender systems"
    - "predicting multicellular function through multi layer tissue networks"
    - "semi supervised classification with graph convolutional networks"
    - "the more you know using knowledge graphs for image classification"

Connected through hyperedge HE-4 (induced by source paper "multi scale self attention for text classification"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "star transformer"
    - "sequence labeling a practical approach"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "hierarchically refined label attention network for sequence labeling"):
  with members:
    - "benchmarking approximate inference methods for neural structured prediction"
    - "design challenges and misconceptions in neural sequence labeling"
    - "two local models for neural constituent parsing"
    - "joint embedding of words and labels for text classification"
    - "semi supervised sequence modeling with cross view training"
    - "cross target stance classification with self attention networks"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning approximate inference networks for structured prediction"

Connected through hyperedge HE-6 (induced by source paper "hierarchical contextualized representation for named entity recognition"):
  with members:
    - "semantics aware bert for language understanding"
    - "grn gated relation network to enhance convolutional neural network for named entity recognition"
    - "syntax aware multilingual semantic role labeling"
    - "lattice based transformer encoder for neural machine translation"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "dual co matching network for multi choice reading comprehension"
    - "robust lexical features for improved neural network named entity recognition"
    - "contextualized non local neural networks for sequence learning"

Connected through hyperedge HE-7 (induced by source paper "densely connected graph convolutional networks for graph to sequence learning"):
  with members:
    - "structural neural encoders for amr to text generation"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph to sequence learning using gated graph neural networks"
    - "deep contextualized word representations"
    - "a graph to sequence model for amr to text generation"
    - "deep graph convolutional encoders for structured data to text generation"

Connected through hyperedge HE-8 (induced by source paper "spatio temporal video re localization by warp lstm"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "a better baseline for ava"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "actor centric relation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "reconstruction network for video captioning"

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)