human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "zero shot adaptive transfer for conversational language understanding"
  Abstract: Conversational agents such as Alexa and Google Assistant constantly need to increase their language understanding capabilities by adding new domains. A massive amount of labeled data is required for training each new domain. While domain adaptation approaches alleviate the annotation cost, prior approaches suffer from increased training time and suboptimal concept alignments. To tackle this, we introduce a novel Zero-Shot Adaptive Transfer method for slot tagging that utilizes the slot description for transferring reusable concepts across domains, and enjoys efficient training without any explicit concept alignments. Extensive experimentation over a dataset of 10 domains relevant to our commercial personal digital assistant shows that our model outperforms previous state-of-the-art systems by a large margin, and achieves an even higher improvement in the low data regime.

Connected through hyperedge HE-1 (induced by source paper "fine tuning bert for schema guided zero shot dialogue state tracking"):
  with members:
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "building a conversational agent overnight with dialogue self play"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "an end to end trainable neural network model with belief tracking for task oriented dialog"

Connected through hyperedge HE-2 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "zero shot user intent detection via capsule neural networks"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for context aware spoken language understanding"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "deep contextualized word representations"
    - "an efficient approach to encoding context for spoken language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-4 (induced by source paper "data augmentation with atomic templates for spoken language understanding"):
  with members:
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "sequence to sequence data augmentation for dialogue language understanding"
    - "towards zero shot frame semantic parsing for domain scaling"
    - "concept transfer learning for adaptive language understanding"
    - "pointing the unknown words"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

Question: Please tell me which class the center node 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)