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 "adaptive convolutional filter generation for natural language understanding"):
  Abstract: Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP are not expressive enough, in the sense that all input sentences share the same learned (and static) set of filters. Motivated by this problem, we propose an adaptive convolutional filter generation framework for natural language understanding, by leveraging a meta network to generate input-aware filters. We further generalize our framework to model question-answer sentence pairs and propose an adaptive question answering (AdaQA) model; a novel two-way feature abstraction mechanism is introduced to encapsulate co-dependent sentence representations. We investigate the effectiveness of our framework on document categorization and answer sentence-selection tasks, achieving state-of-the-art performance on several benchmark datasets.

Includes members:
  Member "multi view sentence representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved semantic aware network embedding with fine grained word alignment")
  Member "nash toward end to end neural architecture for generative semantic hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to hash with graph neural networks for recommender systems")
  Member "joint embedding of words and labels for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "description based text classification with reinforcement learning")
  Member "diffusion maps for textual network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dialogue history integration into end to end signal to concept spoken language understanding systems")
  Member "topic compositional neural language model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformer xl attentive language models beyond a fixed length context")
  Member "deconvolutional latent variable model for text sequence matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "topic guided variational autoencoders for text generation")
  Member "dynamic compositional neural networks over tree structure":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an enhanced tree lstm architecture for sentence semantic modeling using typed dependencies")
  Member "learning loss functions for semi supervised learning via discriminative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "yes we gan applying adversarial techniques for autonomous driving")

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)