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: "a re ranker scheme for integrating large scale nlu models"
  Abstract: Large scale Natural Language Understanding (NLU) systems are typically trained on large quantities of data, requiring a fast and scalable training strategy. A typical design for NLU systems consists of domain-level NLU modules (domain classifier, intent classifier and named entity recognizer). Hypotheses (NLU interpretations consisting of various intent+slot combinations) from these domain specific modules are typically aggregated with another downstream component. The re-ranker integrates outputs from domain-level recognizers, returning a scored list of cross domain hypotheses. An ideal re-ranker will exhibit the following two properties: (a) it should prefer the most relevant hypothesis for the given input as the top hypothesis and, (b) the interpretation scores corresponding to each hypothesis produced by the re-ranker should be calibrated. Calibration allows the final NLU interpretation score to be comparable across domains. We propose a novel re-ranker strategy that addresses these aspects, while also maintaining domain specific modularity. We design optimization loss functions for such a modularized re-ranker and present results on decreasing the top hypothesis error rate as well as maintaining the model calibration. We also experiment with an extension involving training the domain specific re-rankers on datasets curated independently by each domain to allow further asynchronization. %The proposed re-ranker design showcases the following: (i) improved NLU performance over an unweighted aggregation strategy, (ii) cross-domain calibrated performance and, (iii) support for use cases involving training each re-ranker on datasets curated by each domain independently.

Connected through hyperedge HE-1 (induced by source paper "fast intent classification for spoken language understanding"):
  with members:
    - "one vs all models for asynchronous training an empirical analysis"
    - "statistical model compression for small footprint natural language understanding"
    - "spoken language understanding on the edge"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "knowledge distillation for small footprint highway networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "efficient semi supervised learning for natural language understanding by optimizing diversity"):
  with members:
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep active learning for named entity recognition"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-3 (induced by source paper "f10 sgd fast training of elastic net linear models for text classification and named entity recognition"):
  with members:
    - "fasttext zip compressing text classification models"
    - "bag of tricks for efficient text classification"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-4 (induced by source paper "one vs all models for asynchronous training an empirical analysis"):
  with members:
    - "convolutional neural network architectures for matching natural language sentences"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "active learning for new domains in natural language understanding"):
  with members:
    - "bidirectional lstm crf models for sequence tagging"

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)