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 hierarchical framework for relation extraction with reinforcement learning"
  Abstract: Most existing methods determine relation types only after all the entities have been recognized, thus the interaction between relation types and entity mentions is not fully modeled. This paper presents a novel paradigm to deal with relation extraction by regarding the related entities as the arguments of a relation. We apply a hierarchical reinforcement learning (HRL) framework in this paradigm to enhance the interaction between entity mentions and relation types. The whole extraction process is decomposed into a hierarchy of two-level RL policies for relation detection and entity extraction respectively, so that it is more feasible and natural to deal with overlapping relations. Our model was evaluated on public datasets collected via distant supervision, and results show that it gains better performance than existing methods and is more powerful for extracting overlapping relations.

Connected through hyperedge HE-1 (induced by source paper "entity relation extraction as multi turn question answering"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial training for multi context joint entity and relation extraction"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "learning to infer entities properties and their relations from clinical conversations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "extracting symptoms and their status from clinical conversations"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi labeled relation extraction with attentive capsule network"
    - "multi task learning for sequence tagging an empirical study"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "improving distant supervised relation extraction by dynamic neural network"):
  with members:
    - "global textual relation embedding for relational understanding"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "cross relation cross bag attention for distantly supervised relation extraction"
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "multi labeled relation extraction with attentive capsule network"
    - "combining distant and direct supervision for neural relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-4 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "effective attention modeling for neural relation extraction"
    - "end to end neural relation extraction using deep biaffine attention"
    - "attention guided graph convolutional networks for relation extraction"
    - "a question focused multi factor attention network for question answering"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "deep graph convolutional encoders for structured data to text generation"

Connected through hyperedge HE-5 (induced by source paper "a hierarchical reinforced sequence operation method for unsupervised text style transfer"):
  with members:
    - "a dual reinforcement learning framework for unsupervised text style transfer"
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "an operation sequence model for explainable neural machine translation"
    - "reinforcement learning for relation classification from noisy data"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "joint extraction of entities and relations based on a novel decomposition strategy"):
  with members:
    - "porous lattice based transformer encoder for chinese ner"
    - "entity relation extraction as multi turn question answering"
    - "open domain targeted sentiment analysis via span based extraction and classification"
    - "sentiment tagging with partial labels using modular architectures"
    - "adversarial training for multi context joint entity and relation extraction"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "heterogeneous supervision for relation extraction a representation learning approach"
    - "empower sequence labeling with task aware neural language model"

Connected through hyperedge HE-7 (induced by source paper "joint extraction of entities and relations with a hierarchical multi task tagging model"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "design challenges and misconceptions in neural sequence labeling"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "semi supervised multitask learning for sequence labeling"
    - "an overview of multi task learning in deep neural networks"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-8 (induced by source paper "aggregating e commerce search results from heterogeneous sources via hierarchical reinforcement learning"):
  with members:
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "globally optimized mutual influence aware ranking in e commerce search"
    - "ranking for relevance and display preferences in complex presentation layouts"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "a deep hierarchical approach to lifelong learning in minecraft"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "dueling network architectures for deep reinforcement learning"

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)