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 language model adaptation for conversational agents"
  Abstract: Statistical language models (LM) play a key role in Automatic Speech Recognition (ASR) systems used by conversational agents. These ASR systems should provide a high accuracy under a variety of speaking styles, domains, vocabulary and argots. In this paper, we present a DNN-based method to adapt the LM to each user-agent interaction based on generalized contextual information, by predicting an optimal, context-dependent set of LM interpolation weights. We show that this framework for contextual adaptation provides accuracy improvements under different possible mixture LM partitions that are relevant for both (1) Goal-oriented conversational agents where it's natural to partition the data by the requested application and for (2) Non-goal oriented conversational agents where the data can be partitioned using topic labels that come from predictions of a topic classifier. We obtain a relative WER improvement of 3% with a 1-pass decoding strategy and 6% in a 2-pass decoding framework, over an unadapted model. We also show up to a 15% relative improvement in recognizing named entities which is of significant value for conversational ASR systems.

Connected through hyperedge HE-1 (induced by source paper "agnostic federated learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "minimax statistical learning with wasserstein distances"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "training gans with optimism"
    - "federated multi task learning"
    - "equality of opportunity in supervised learning"
    - "distributed mean estimation with limited communication"

Connected through hyperedge HE-2 (induced by source paper "scalable multi corpora neural language models for asr"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "self normalization properties of language modeling"
    - "scalable language model adaptation for spoken dialogue systems"
    - "how transferable are neural networks in nlp applications"
    - "exploring the limits of language modeling"
    - "deep residual learning for image recognition"
    - "strategies for training large vocabulary neural language models"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"

Connected through hyperedge HE-3 (induced by source paper "advancing the state of the art in open domain dialog systems through the alexa prize"):
  with members:
    - "slugbot developing a computational model andframework of a novel dialogue genre"
    - "achieving human parity on automatic chinese to english news translation"
    - "alquist the alexa prize socialbot"
    - "topic based evaluation for conversational bots"
    - "slugbot an application of a novel and scalable open domain socialbot framework"
    - "tartan a retrieval based socialbot powered by a dynamic finite state machine architecture"
    - "conversational ai the science behind the alexa prize"
    - "just ask building an architecture for extensible self service spoken language understanding"

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)