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 "an interactive multi task learning network for end to end aspect based sentiment analysis"):
  Abstract: Aspect-based sentiment analysis produces a list of aspect terms and their corresponding sentiments for a natural language sentence. This task is usually done in a pipeline manner, with aspect term extraction performed first, followed by sentiment predictions toward the extracted aspect terms. While easier to develop, such an approach does not fully exploit joint information from the two subtasks and does not use all available sources of training information that might be helpful, such as document-level labeled sentiment corpus. In this paper, we propose an interactive multi-task learning network (IMN) which is able to jointly learn multiple related tasks simultaneously at both the token level as well as the document level. Unlike conventional multi-task learning methods that rely on learning common features for the different tasks, IMN introduces a message passing architecture where information is iteratively passed to different tasks through a shared set of latent variables. Experimental results demonstrate superior performance of the proposed method against multiple baselines on three benchmark datasets.

Includes members:
  Member "double embeddings and cnn based sequence labeling for aspect extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "transformation networks for target oriented sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "exploiting document knowledge for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting bert for end to end aspect based sentiment analysis")
  Member "a unified model for opinion target extraction and target sentiment prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting bert for end to end aspect based sentiment analysis")
  Member "aspect term extraction with history attention and selective transformation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial training for aspect based sentiment analysis with bert")
  Member "learning to attend via word aspect associative fusion for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting bert for end to end aspect based sentiment analysis")
  Member "recursive neural conditional random fields for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "snippext semi supervised opinion mining with augmented data")

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)