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 "transferable end to end aspect based sentiment analysis with selective adversarial learning"):
  Abstract: Joint extraction of aspects and sentiments can be effectively formulated as a sequence labeling problem. However, such formulation hinders the effectiveness of supervised methods due to the lack of annotated sequence data in many domains. To address this issue, we firstly explore an unsupervised domain adaptation setting for this task. Prior work can only use common syntactic relations between aspect and opinion words to bridge the domain gaps, which highly relies on external linguistic resources. To resolve it, we propose a novel Selective Adversarial Learning (SAL) method to align the inferred correlation vectors that automatically capture their latent relations. The SAL method can dynamically learn an alignment weight for each word such that more important words can possess higher alignment weights to achieve fine-grained (word-level) adaptation. Empirically, extensive experiments demonstrate the effectiveness of the proposed SAL method.

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 "exploiting document knowledge for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "adversarial training for aspect based sentiment analysis with bert")
  Member "tensorized self attention efficiently modeling pairwise and global dependencies together":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fastfusionnet new state of the art for dawnbench squad")
  Member "adaptive semi supervised learning for cross domain sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving domain adapted sentiment classification by deep adversarial mutual learning")
  Member "interactive attention networks for aspect level sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network")

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)