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: "investigating capsule networks with dynamic routing for text classification"
  Abstract: In this study, we explore capsule networks with dynamic routing for text classification. We propose three strategies to stabilize the dynamic routing process to alleviate the disturbance of some noise capsules which may contain "background" information or have not been successfully trained. A series of experiments are conducted with capsule networks on six text classification benchmarks. Capsule networks achieve state of the art on 4 out of 6 datasets, which shows the effectiveness of capsule networks for text classification. We additionally show that capsule networks exhibit significant improvement when transfer single-label to multi-label text classification over strong baseline methods. To the best of our knowledge, this is the first work that capsule networks have been empirically investigated for text modeling.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "deconvolution based global decoding for neural machine translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "self attention based bigru and capsule network for named entity recognition"):
  with members:
    - "can ner convolutional attention network for chinese named entity recognition"
    - "chinese ner using lattice lstm"
    - "a survey on deep learning for named entity recognition"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "subspace capsule network"):
  with members:
    - "dual directed capsule network for very low resolution image recognition"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved techniques for training gans"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "deconvolution based global decoding for neural machine translation"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "information aggregation via dynamic routing for sequence encoding"
    - "a hybrid word character model for abstractive summarization"

Connected through hyperedge HE-5 (induced by source paper "improving multi head attention with capsule networks"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "information aggregation for multi head attention with routing by agreement"
    - "weighted transformer network for machine translation"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "self attention with relative position representations"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "scaling neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "capsulevos semi supervised video object segmentation using capsule routing"):
  with members:
    - "rvos end to end recurrent network for video object segmentation"
    - "videocapsulenet a simplified network for action detection"
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "youtube vos sequence to sequence video object segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"

Connected through hyperedge HE-7 (induced by source paper "induction networks for few shot text classification"):
  with members:
    - "analogical reasoning on chinese morphological and semantic relations"
    - "lifelong domain word embedding via meta learning"
    - "zero shot user intent detection via capsule neural networks"
    - "diverse few shot text classification with multiple metrics"
    - "low shot learning with imprinted weights"
    - "prototypical networks for few shot learning"
    - "dynamic routing between capsules"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-8 (induced by source paper "transformation of dense and sparse text representations"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "consistent dialogue generation with self supervised feature learning"
    - "xsense learning sense separated sparse representations and textual definitions for explainable word sense networks"
    - "attention is all you need"
    - "sparse representation based open set recognition"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "spine sparse interpretable neural embeddings"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)