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: "classifying relations via long short term memory networks along shortest dependency path"
  Abstract: Relation classification is an important research arena in the field of natural language processing (NLP). In this paper, we present SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (LSTM) units, pick up heterogeneous information along the SDP. Our proposed model has several distinct features: (1) The shortest dependency paths retain most relevant information (to relation classification), while eliminating irrelevant words in the sentence. (2) The multichannel LSTM networks allow effective information integration from heterogeneous sources over the dependency paths. (3) A customized dropout strategy regularizes the neural network to alleviate overfitting. We test our model on the SemEval 2010 relation classification task, and achieve an $F_1$-score of 83.7\%, higher than competing methods in the literature.

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 "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-3 (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"
    - "n ary relation extraction using graph state lstm"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"

Connected through hyperedge HE-4 (induced by source paper "neural snowball for few shot relation learning"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "deep comparison relation columns for few shot learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "self ensembling for visual domain adaptation"
    - "unsupervised open relation extraction"

Connected through hyperedge HE-5 (induced by source paper "syntax aware neural semantic role labeling"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "tag enhanced tree structured neural networks for implicit discourse relation classification"
    - "deep contextualized word representations"
    - "linguistically informed self attention for semantic role labeling"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "improved neural machine translation with a syntax aware encoder and decoder"

Connected through hyperedge HE-6 (induced by source paper "effective attention modeling for neural relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving distantly supervised relation extraction with neural noise converter and conditional optimal selector"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "attention is all you need"
    - "improving slot filling performance with attentive neural networks on dependency structures"

Connected through hyperedge HE-7 (induced by source paper "fine tuning bert for joint entity and relation extraction in chinese medical text"):
  with members:
    - "fine grained named entity recognition using elmo and wikidata"
    - "improving relation extraction by pre trained language representations"
    - "deep contextualized word representations"
    - "automatic severity classification of coronary artery disease via recurrent capsule network"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "a survey of deep learning methods for relation extraction"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"

Connected through hyperedge HE-8 (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"

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)