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: "zero shot user intent detection via capsule neural networks"
  Abstract: User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENT-CAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.

Connected through hyperedge HE-1 (induced by source paper "detecting fake news with capsule neural networks"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "information aggregation via dynamic routing for sequence encoding"
    - "fake news detection in social networks via crowd signals"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "dynamic routing between capsules"
    - "automatic detection of fake news"

Connected through hyperedge HE-2 (induced by source paper "relation module for non answerable prediction on question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-3 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "fast and scalable expansion of natural language understanding functionality for intelligent agents"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"

Connected through hyperedge HE-4 (induced by source paper "towards scalable multi domain conversational agents the schema guided dialogue dataset"):
  with members:
    - "hyst a hybrid approach for flexible and accurate dialogue state tracking"
    - "bert dst scalable end to end dialogue state tracking with bidirectional encoder representations from transformer"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "robust zero shot cross domain slot filling with example values"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "towards universal dialogue state tracking"
    - "building a conversational agent overnight with dialogue self play"
    - "multi task learning for joint language understanding and dialogue state tracking"

Connected through hyperedge HE-5 (induced by source paper "towards open intent discovery for conversational text"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "onenet joint domain intent slot prediction for spoken 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"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "multi domain adversarial learning for slot filling in spoken language understanding"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-6 (induced by source paper "capturing argument interaction in semantic role labeling with capsule networks"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "dependency or span end to end uniform semantic role labeling"
    - "polyglot semantic role labeling"
    - "multi labeled relation extraction with attentive capsule network"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "multi grained named entity recognition"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a neural transition based model for nested mention recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "labeling gaps between words recognizing overlapping mentions with mention separators"

Connected through hyperedge HE-8 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "towards linear time neural machine translation with capsule networks"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "dynamic routing between capsules"
    - "attentive pooling networks"
    - "simple question answering by attentive convolutional neural network"

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)