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 "concept transfer learning for adaptive language understanding"):
  Abstract: Concept definition is important in language understanding (LU) adaptation since literal definition difference can easily lead to data sparsity even if different data sets are actually semantically correlated. To address this issue, in this paper, a novel concept transfer learning approach is proposed. Here, substructures within literal concept definition are investigated to reveal the relationship between concepts. A hierarchical semantic representation for concepts is proposed, where a semantic slot is represented as a composition of {\em atomic concepts}. Based on this new hierarchical representation, transfer learning approaches are developed for adaptive LU. The approaches are applied to two tasks: value set mismatch and domain adaptation, and evaluated on two LU benchmarks: ATIS and DSTC 2\&3. Thorough empirical studies validate both the efficiency and effectiveness of the proposed method. In particular, we achieve state-of-the-art performance ($F_1$-score 96.08\%) on ATIS by only using lexicon features.

Includes members:
  Member "neural models for sequence chunking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid neural tagging model for open relation extraction")
  Member "sequential convolutional neural networks for slot filling in spoken language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving slot filling by utilizing contextual information")
  Member "leveraging sentence level information with encoder lstm for semantic slot filling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation")
  Member "encoder decoder with focus mechanism for sequence labelling based spoken language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint contextual modeling for asr correction and language understanding")
  Member "attention based recurrent neural network models for joint intent detection and slot filling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing")
  Member "recurrent neural networks with external memory for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving slot filling by utilizing contextual information")
  Member "neural machine translation by jointly learning to align and translate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)