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: "contextual slot carryover for disparate schemas"
  Abstract: In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context. In large-scale multi-domain systems, this presents two challenges - scaling to a very large and potentially unbounded set of slot values, and dealing with diverse schemas. We present a neural network architecture that addresses the slot value scalability challenge by reformulating the contextual interpretation as a decision to carryover a slot from a set of possible candidates. To deal with heterogenous schemas, we introduce a simple data-driven method for trans- forming the candidate slots. Our experiments show that our approach can scale to multiple domains and provides competitive results over a strong baseline.

Connected through hyperedge HE-1 (induced by source paper "adjusting image attributes of localized regions with low level dialogue"):
  with members:
    - "a multimodal dialogue system for conversational image editing"
    - "expressing visual relationships via language"
    - "learning to globally edit images with textual description"
    - "learning to describe differences between pairs of similar images"
    - "a corpus for reasoning about natural language grounded in photographs"
    - "a system for automated image editing from natural language commands"
    - "mattnet modular attention network for referring expression comprehension"
    - "sequential attention gan for interactive image editing via dialogue"

Connected through hyperedge HE-2 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "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 "smart home personal assistants a security and privacy review"):
  with members:
    - "intelligent virtual assistant knows your life"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "user perceptions of smart home iot privacy"
    - "understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "comparing speech and keyboard text entry for short messages in two languages on touchscreen phones"
    - "continuous authentication for voice assistants"

Connected through hyperedge HE-4 (induced by source paper "a dataset for resolving referring expressions in spoken dialogue via contextual query rewrites cqr"):
  with members:
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "an efficient approach to encoding context for spoken language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "scalable multi domain dialogue state tracking"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"
    - "learning to attend copy and generate for session based query suggestion"

Connected through hyperedge HE-5 (induced by source paper "improving long distance slot carryover in spoken dialogue systems"):
  with members:
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "scalable multi domain dialogue state tracking"
    - "attention is all you need"
    - "neural belief tracker data driven dialogue state tracking"
    - "incremental lstm based dialog state tracker"

Connected through hyperedge HE-6 (induced by source paper "scaling multi domain dialogue state tracking via query reformulation"):
  with members:
    - "a dataset for resolving referring expressions in spoken dialogue via contextual query rewrites cqr"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "scalable multi domain dialogue state tracking"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-7 (induced by source paper "leveraging external knowledge for out of vocabulary entity labeling"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "multi context label embedding"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "prototypical networks for few shot learning"
    - "poincar e embeddings for learning hierarchical representations"
    - "adam a method for stochastic optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "time masking leveraging temporal information in spoken dialogue systems"):
  with members:
    - "scalable multi domain dialogue state tracking"
    - "dynamic time aware attention to speaker roles and contexts for spoken language understanding"
    - "context dependent word representation for neural machine translation"
    - "neural belief tracker data driven dialogue state tracking"

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)