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: "semantic relation classification via convolutional neural networks with simple negative sampling"
  Abstract: Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from the shortest dependency path through a convolution neural network. We further propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-the-art methods on the SemEval-2010 Task 8 dataset.

Connected through hyperedge HE-1 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "mile a multi level framework for scalable graph embedding"

Connected through hyperedge HE-2 (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-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"
    - "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-5 (induced by source paper "fine tuning pre trained transformer language models to distantly supervised relation extraction"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "generating wikipedia by summarizing long sequences"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "see syntax aware entity embedding for neural relation extraction"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-6 (induced by source paper "a general framework for information extraction using dynamic span graphs"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"

Connected through hyperedge HE-7 (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-8 (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"
    - "a dependency based neural network for relation classification"

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)