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 "can constrained attention networks for multi aspect sentiment analysis"):
  Abstract: Aspect level sentiment classification is a fine-grained sentiment analysis task. To detect the sentiment towards a particular aspect in a sentence, previous studies have developed various attention-based methods for generating aspect-specific sentence representations. However, the attention may inherently introduce noise and downgrade the performance. In this paper, we propose constrained attention networks (CAN), a simple yet effective solution, to regularize the attention for multi-aspect sentiment analysis, which alleviates the drawback of the attention mechanism. Specifically, we introduce orthogonal regularization on multiple aspects and sparse regularization on each single aspect. Experimental results on two public datasets demonstrate the effectiveness of our approach. We further extend our approach to multi-task settings and outperform the state-of-the-art methods.

Includes members:
  Member "transformation networks for target oriented sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "aspect level sentiment classification with attention over attention neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "aspect based sentiment analysis with gated convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "component analysis for visual question answering architectures")
  Member "sparse and constrained attention for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning credible deep neural networks with rationale regularization")
  Member "learning to attend via word aspect associative fusion for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting bert for end to end aspect based sentiment analysis")
  Member "a structured self attentive sentence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high order paired aspp networks for semantic segmenation")
  Member "interactive attention networks for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")
  Member "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mix review alleviate forgetting in the pretrain finetune framework for neural language generation models")

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)