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: "attention based capsule networks with dynamic routing for relation extraction"
  Abstract: A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we show that capsule networks improve multiple entity pairs relation extraction.

Connected through hyperedge HE-1 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"

Connected through hyperedge HE-2 (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"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"
    - "dtmt a novel deep transition architecture for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "sgm sequence generation model for multi label classification"
    - "graph convolutional networks for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "capsule networks against medical imaging data challenges"
    - "dynamic routing between capsules"
    - "adversarial multi task learning for text classification"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-4 (induced by source paper "a capsule network for recommendation and explaining what you like and dislike"):
  with members:
    - "investigating capsule networks with dynamic routing for text classification"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "multi pointer co attention networks for recommendation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "zero shot user intent detection via capsule neural networks"
    - "neural factorization machines for sparse predictive analytics"
    - "dynamic routing between capsules"
    - "joint deep modeling of users and items using reviews for recommendation"

Connected through hyperedge HE-5 (induced by source paper "context aware deep model for entity recommendation in search engine at alibaba"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fasttext zip compressing text classification models"
    - "adaptive sampled softmax with kernel based sampling"
    - "node2vec scalable feature learning for networks"
    - "querying knowledge graphs by example entity tuples"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "capsule attention for multimodal eeg and eog spatiotemporal representation learning with application to driver vigilance estimation"):
  with members:
    - "classification of hand movements from eeg using a deep attention based lstm network"
    - "lstm a search space odyssey"
    - "dynamic routing between capsules"
    - "a multimodal approach to estimating vigilance using eeg and forehead eog"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "ipre a dataset for inter personal relationship extraction"):
  with members:
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "see syntax aware entity embedding for neural relation extraction"
    - "reinforcement learning for relation classification from noisy data"
    - "adam a method for stochastic optimization"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

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.IR(Information Retrieval)