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: "a dependency based neural network for relation classification"
  Abstract: Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees. In this paper, we further explore how to make full use of the combination of these dependency information. We first propose a new structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop dependency-based neural networks (DepNN): a recursive neural network designed to model the subtrees, and a convolutional neural network to capture the most important features on the shortest path. Experiments on the SemEval-2010 dataset show that our proposed method achieves state-of-art results.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (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-5 (induced by source paper "semantic relation classification via bidirectional lstm networks with entity aware attention using latent entity typing"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "classifying relations by ranking with convolutional neural networks"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"

Connected through hyperedge HE-6 (induced by source paper "inter sentence relation extraction with document level graph convolutional neural network"):
  with members:
    - "dating documents using graph convolution networks"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "cross sentence n ary relation extraction with graph lstms"
    - "graph based neural multi document summarization"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "unsupervised ranking model for entity coreference resolution"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "classifying medical relations in clinical text via convolutional neural networks"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "drug drug interaction extraction from biomedical text using long short term memory network"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"
    - "local translation prediction with global sentence representation"
    - "classifying relations via long short term memory networks along shortest dependency path"

Connected through hyperedge HE-8 (induced by source paper "uncover the ground truth relations in distant supervision a neural expectation maximization framework"):
  with members:
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "reinforcement learning for relation classification from noisy data"
    - "weakly supervised relation extraction by pattern enhanced embedding learning"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "neural expectation maximization"
    - "jointly extracting relations with class ties via effective deep ranking"
    - "end to end relation extraction using lstms on sequences and tree structures"

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)