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: "n ary relation extraction using graph state lstm"
  Abstract: Cross-sentence $n$-ary relation extraction detects relations among $n$ entities across multiple sentences. Typical methods formulate an input as a \textit{document graph}, integrating various intra-sentential and inter-sentential dependencies. The current state-of-the-art method splits the input graph into two DAGs, adopting a DAG-structured LSTM for each. Though being able to model rich linguistic knowledge by leveraging graph edges, important information can be lost in the splitting procedure. We propose a graph-state LSTM model, which uses a parallel state to model each word, recurrently enriching state values via message passing. Compared with DAG LSTMs, our graph LSTM keeps the original graph structure, and speeds up computation by allowing more parallelization. On a standard benchmark, our model shows the best result in the literature.

Connected through hyperedge HE-1 (induced by source paper "mining commonsense facts from the physical world"):
  with members:
    - "joint detection and location of english puns"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "aligning cross lingual entities with multi aspect information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "learning cross lingual distributed logical representations for semantic parsing"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "automatic extraction of commonsense locatednear knowledge"
    - "fast and accurate entity recognition with iterated dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "recurrent graph syntax encoder for neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "semantic neural machine translation using amr"
    - "weighted transformer network for machine translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "leveraging dependency forest for neural medical relation extraction"):
  with members:
    - "graph based neural sentence ordering"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a silver standard corpus of human phenotype gene relations"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "semantic neural machine translation using amr"
    - "attention guided graph convolutional networks for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-4 (induced by source paper "attention guided graph convolutional networks for relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-5 (induced by source paper "extracting multiple relations in one pass with pre trained transformers"):
  with members:
    - "language conditioned graph networks for relational reasoning"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "self attention with relative position representations"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "sirius ltg uio at semeval 2018 task 7 convolutional neural networks with shortest dependency paths for semantic relation extraction and classification in scientific papers"

Connected through hyperedge HE-6 (induced by source paper "graph convolution for multimodal information extraction from visually rich documents"):
  with members:
    - "chargrid towards understanding 2d documents"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "image generation from scene graphs"
    - "cross sentence n ary relation extraction with graph lstms"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "cloudscan a configuration free invoice analysis system using recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "graph based neural sentence ordering"):
  with members:
    - "semantic neural machine translation using amr"
    - "graph to sequence learning using gated graph neural networks"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a graph to sequence model for amr to text generation"
    - "sentence state lstm for text representation"
    - "attention is all you need"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "graphene a precise biomedical literature retrieval engine with graph augmented deep learning and external knowledge empowerment"):
  with members:
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "a fast deep learning model for textual relevance in biomedical information retrieval"
    - "graphie a graph based framework for information extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a neural multi task learning framework to jointly model medical named entity recognition and normalization"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "pacrr a position aware neural ir model for relevance matching"

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)