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: "unsupervised dialogue act induction using gaussian mixtures"
  Abstract: This paper introduces a new unsupervised approach for dialogue act induction. Given the sequence of dialogue utterances, the task is to assign them the labels representing their function in the dialogue. #R##N#Utterances are represented as real-valued vectors encoding their meaning. We model the dialogue as Hidden Markov model with emission probabilities estimated by Gaussian mixtures. We use Gibbs sampling for posterior inference. #R##N#We present the results on the standard Switchboard-DAMSL corpus. Our algorithm achieves promising results compared with strong supervised baselines and outperforms other unsupervised algorithms.

Connected through hyperedge HE-1 (induced by source paper "a post processing method for detecting unknown intent of dialogue system via pre trained deep neural network classifier"):
  with members:
    - "deep unknown intent detection with margin loss"
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "doc deep open classification of text documents"
    - "open category classification by adversarial sample generation"

Connected through hyperedge HE-2 (induced by source paper "deep unknown intent detection with margin loss"):
  with members:
    - "a bi model based rnn semantic frame parsing model for intent detection and slot filling"
    - "joint learning of domain classification and out of domain detection with dynamic class weighting for satisficing false acceptance rates"
    - "additive margin softmax for face verification"
    - "doc deep open classification of text documents"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "open category classification by adversarial sample generation"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-3 (induced by source paper "cross lingual word analogies using linear transformations between semantic spaces"):
  with members:
    - "evaluation of croatian word embeddings"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "cross lingual models of word embeddings an empirical comparison"
    - "a strong baseline for learning cross lingual word embeddings from sentence alignments"
    - "enriching word vectors with subword information"
    - "massively multilingual word embeddings"
    - "new word analogy corpus for exploring embeddings of czech words"
    - "issues in evaluating semantic spaces using word analogies"

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)