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: "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
  Abstract: Word embeddings have been widely adopted across several NLP applications. Most existing word embedding methods utilize sequential context of a word to learn its embedding. While there have been some attempts at utilizing syntactic context of a word, such methods result in an explosion of the vocabulary size. In this paper, we overcome this problem by proposing SynGCN, a flexible Graph Convolution based method for learning word embeddings. SynGCN utilizes the dependency context of a word without increasing the vocabulary size. Word embeddings learned by SynGCN outperform existing methods on various intrinsic and extrinsic tasks and provide an advantage when used with ELMo. We also propose SemGCN, an effective framework for incorporating diverse semantic knowledge for further enhancing learned word representations. We make the source code of both models available to encourage reproducible research.

Connected through hyperedge HE-1 (induced by source paper "tensor graph convolutional networks for text classification"):
  with members:
    - "mr gnn multi resolution and dual graph neural network for predicting structured entity interactions"
    - "multi channel graph neural network for entity alignment"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "beyond visual semantics exploring the role of scene text in image understanding"):
  with members:
    - "towards vqa models that can read"
    - "exploring hate speech detection in multimodal publications"
    - "relation aware graph attention network for visual question answering"
    - "equal but not the same understanding the implicit relationship between persuasive images and text"
    - "fots fast oriented text spotting with a unified network"
    - "textually enriched neural module networks for visual question answering"
    - "don t only feel read using scene text to understand advertisements"
    - "understanding visual ads by aligning symbols and objects using co attention"

Connected through hyperedge HE-3 (induced by source paper "using dynamic embeddings to improve static embeddings"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "simple bert models for relation extraction and semantic role labeling"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "linguistic knowledge and transferability of contextual representations"
    - "roberta a robustly optimized bert pretraining approach"
    - "design challenges and misconceptions in neural sequence labeling"

Connected through hyperedge HE-4 (induced by source paper "graph to graph transformer for transition based dependency parsing"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "assessing bert s syntactic abilities"
    - "recurrent graph syntax encoder for neural machine translation"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross sentence n ary relation extraction with graph lstms"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "integrating lexical knowledge in word embeddings using sprinkling and retrofitting"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "counter fitting word vectors to linguistic constraints"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-6 (induced by source paper "hypergcn a new method of training graph convolutional networks on hypergraphs"):
  with members:
    - "graph colouring meets deep learning effective graph neural network models for combinatorial problems"
    - "exact k recommendation via maximal clique optimization"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "multi label image recognition with graph convolutional networks"
    - "approximate k cover in hypergraphs efficient algorithms and applications"
    - "planted hitting set recovery in hypergraphs"
    - "a comprehensive survey on graph neural networks"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

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)