human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graph convolution over pruned dependency trees improves relation extraction"):
  Abstract: Dependency trees help relation extraction models capture long-range relations between words. However, existing dependency-based models either neglect crucial information (e.g., negation) by pruning the dependency trees too aggressively, or are computationally inefficient because it is difficult to parallelize over different tree structures. We propose an extension of graph convolutional networks that is tailored for relation extraction, which pools information over arbitrary dependency structures efficiently in parallel. To incorporate relevant information while maximally removing irrelevant content, we further apply a novel pruning strategy to the input trees by keeping words immediately around the shortest path between the two entities among which a relation might hold. The resulting model achieves state-of-the-art performance on the large-scale TACRED dataset, outperforming existing sequence and dependency-based neural models. We also show through detailed analysis that this model has complementary strengths to sequence models, and combining them further improves the state of the art.

Includes members:
  Member "end to end neural coreference resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain independent extraction of scientific concepts from research articles")
  Member "improved neural relation detection for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "a simple neural network module for relational reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "encoding sentences with graph convolutional networks for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmarking graph neural networks")
  Member "distant supervision for relation extraction beyond the sentence boundary":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leveraging dependency forest for neural medical relation extraction")
  Member "end to end relation extraction using lstms on sequences and tree structures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deeper task specificity improves joint entity and relation extraction")
  Member "classifying relations via long short term memory networks along shortest dependency path":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reflex flexible framework for relation extraction in multiple domains")
  Member "improved semantic representations from tree structured long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unbiased scene graph generation from biased training")

Question: Please tell me which class the source paper 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)