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 bi model based rnn semantic frame parsing model for intent detection and slot filling"
  Abstract: Intent detection and slot filling are two main tasks for building a spoken language understanding(SLU) system. Multiple deep learning based models have demonstrated good results on these tasks . The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model. Most of the previous studies, however, either treat the intent detection and slot filling as two separate parallel tasks, or use a sequence to sequence model to generate both semantic tags and intent. Most of these approaches use one (joint) NN based model (including encoder-decoder structure) to model two tasks, hence may not fully take advantage of the cross-impact between them. In this paper, new Bi-model based RNN semantic frame parsing network structures are designed to perform the intent detection and slot filling tasks jointly, by considering their cross-impact to each other using two correlated bidirectional LSTMs (BLSTM). Our Bi-model structure with a decoder achieves state-of-the-art result on the benchmark ATIS data, with about 0.5$\%$ intent accuracy improvement and 0.9 $\%$ slot filling improvement.

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"
    - "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"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "rapid adaptation of bert for information extraction on domain specific business documents"):
  with members:
    - "navigation based candidate expansion and pretrained language models for citation recommendation"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "bert for joint intent classification and slot filling"
    - "joint slot filling and intent detection via capsule neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"

Connected through hyperedge HE-3 (induced by source paper "cm net a novel collaborative memory network for spoken language understanding"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "simple fast accurate intent classification and slot labeling"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "sentence state lstm for text representation"
    - "memory augmented neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "simple fast accurate intent classification and slot labeling for goal oriented dialogue systems"):
  with members:
    - "position aware self attention with relative positional encodings for slot filling"
    - "self attention with relative position representations"
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "constituency parsing with a self attentive encoder"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "multi lingual intent detection and slot filling in a joint bert based model"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot user intent detection via capsule neural networks"
    - "attention is all you need"
    - "zero shot learning the good the bad and the ugly"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "a stack propagation framework with token level intent detection for spoken language understanding"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "joint slot filling and intent detection via capsule neural networks"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "stochastic multiple choice learning for training diverse deep ensembles"

Connected through hyperedge HE-7 (induced by source paper "deep unknown intent detection with margin loss"):
  with members:
    - "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"
    - "unsupervised dialogue act induction using gaussian mixtures"

Connected through hyperedge HE-8 (induced by source paper "iterative delexicalization for improved spoken language understanding"):
  with members:
    - "onenet joint domain intent slot prediction for spoken language understanding"
    - "coupled representation learning for domains intents and slots in spoken language understanding"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "robust spoken language understanding via paraphrasing"
    - "end to end task completion neural dialogue systems"
    - "language to logical form with neural attention"
    - "attention based recurrent neural network models for joint intent detection and slot filling"

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)