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 "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  Abstract: A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of entities or overlapping entities among the tuples makes it more challenging. Most prior work adopted a pipeline approach where entities were identified first followed by finding the relations among them, thus missing the interaction among the relation tuples in a sentence. In this paper, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations. In the first approach, we propose a representation scheme for relation tuples which enables the decoder to generate one word at a time like machine translation models and still finds all the tuples present in a sentence with full entity names of different length and with overlapping entities. Next, we propose a pointer network-based decoding approach where an entire tuple is generated at every time step. Experiments on the publicly available New York Times corpus show that our proposed approaches outperform previous work and achieve significantly higher F1 scores.

Includes members:
  Member "distant supervision relation extraction with intra bag and inter bag attentions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective attention modeling for neural relation extraction")
  Member "effective attention modeling for neural relation extraction":
    also connected through other hyperedges:
      (none)
  Member "end to end neural relation extraction using deep biaffine attention":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "span based joint entity and relation extraction with transformer pre training")
  Member "attention guided graph convolutional networks for relation extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "event detection with relation aware graph convolutional neural networks")
  Member "a question focused multi factor attention network for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task learning for machine reading comprehension")
  Member "improving distantly supervised relation extraction using word and entity based attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "reside improving distantly supervised neural relation extraction using side information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural graph embedding methods for natural language processing")
  Member "deep graph convolutional encoders for structured data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")

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)