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: "finding consensus in speech recognition word error minimization and other applications of confusion networks"
  Abstract: We describe a new framework for distilling information from word lattices to improve the accuracy of speech recognition and obtain a more perspicuous representation of a set of alternative hypotheses. In the standard MAP decoding approach the recognizer outputs the string of words corresponding to the path with the highest posterior probability given the acoustics and a language model. However, even given optimal models, the MAP decoder does not necessarily minimize the commonly used performance metric, word error rate (WER). We describe a method for explicitly minimizing WER by extracting word hypotheses with the highest posterior probabilities from word lattices. We change the standard problem formulation by replacing global search over a large set of sentence hypotheses with local search over a small set of word candidates. In addition to improving the accuracy of the recognizer, our method produces a new representation of the set of candidate hypotheses that specifies the sequence of word-level confusions in a compact lattice format. We study the properties of confusion networks and examine their use for other tasks, such as lattice compression, word spotting, confidence annotation, and reevaluation of recognition hypotheses using higher-level knowledge sources.

Connected through hyperedge HE-1 (induced by source paper "modeling asr ambiguity for dialogue state tracking using word confusion networks"):
  with members:
    - "curriculum based transfer learning for an effective end to end spoken language understanding and domain portability"
    - "towards end to end spoken language understanding"
    - "towards universal dialogue state tracking"
    - "neural belief tracker data driven dialogue state tracking"

Connected through hyperedge HE-2 (induced by source paper "confusion2vec towards enriching vector space word representations with representational ambiguities"):
  with members:
    - "morphological word embeddings"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "neural lattice language models"
    - "neural lattice to sequence models for uncertain inputs"
    - "enriching word vectors with subword information"
    - "multi view recurrent neural acoustic word embeddings"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-3 (induced by source paper "towards better understanding of spontaneous conversations overcoming automatic speech recognition errors with intent recognition"):
  with members:
    - "composing finite state transducers on gpus"
    - "the capio 2017 conversational speech recognition system"
    - "punctuation prediction model for conversational speech"
    - "the marchex 2018 english conversational telephone speech recognition system"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "the microsoft 2017 conversational speech recognition system"
    - "encodingword confusion networks with recurrent neural networks for dialog state tracking"

Connected through hyperedge HE-4 (induced by source paper "l2rs a learning to rescore mechanism for automatic speech recognition"):
  with members:
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "ted lium 3 twice as much data and corpus repartition for experiments on speaker adaptation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "on a topic model for sentences"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "multi graph decoding for code switching asr"):
  with members:
    - "code switching detection with data augmented acoustic and language models"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "semi supervised acoustic model training for speech with code switching"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "semi tied units for efficient gating in lstm and highway networks"):
  with members:
    - "improved tdnns using deep kernels and frequency dependent grid rnns"
    - "high order recurrent neural networks for acoustic modelling"
    - "very deep convolutional networks for end to end speech recognition"
    - "quasi recurrent neural networks"
    - "learning hidden unit contributions for unsupervised acoustic model adaptation"
    - "fast and accurate recurrent neural network acoustic models for speech recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "small footprint deep neural networks with highway connections for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "improved tdnns using deep kernels and frequency dependent grid rnns"):
  with members:
    - "very deep convolutional neural networks for robust speech recognition"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "deep residual learning for image recognition"
    - "grid long short term memory"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"):
  with members:
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "rasa open source language understanding and dialogue management"
    - "achieving human parity in conversational speech recognition"
    - "efficient estimation of word representations in vector space"
    - "large scale language modeling in automatic speech recognition"

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)