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: "improved relation extraction with feature rich compositional embedding models"
  Abstract: Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "leveraging dependency forest for neural medical relation extraction"):
  with members:
    - "graph based neural sentence ordering"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a silver standard corpus of human phenotype gene relations"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "semantic neural machine translation using amr"
    - "attention guided graph convolutional networks for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-2 (induced by source paper "extracting multiple relations in one pass with pre trained transformers"):
  with members:
    - "language conditioned graph networks for relational reasoning"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "self attention with relative position representations"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "sirius ltg uio at semeval 2018 task 7 convolutional neural networks with shortest dependency paths for semantic relation extraction and classification in scientific papers"

Connected through hyperedge HE-3 (induced by source paper "neural snowball for few shot relation learning"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "deep comparison relation columns for few shot learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "self ensembling for visual domain adaptation"
    - "unsupervised open relation extraction"

Connected through hyperedge HE-4 (induced by source paper "neural cross lingual relation extraction based on bilingual word embedding mapping"):
  with members:
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "multilingual relation extraction using compositional universal schema"
    - "multilingual open relation extraction using cross lingual projection"

Connected through hyperedge HE-5 (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"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a walk based model on entity graphs for relation extraction"):
  with members:
    - "incorporating relation paths in neural relation extraction"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "proje embedding projection for knowledge graph completion"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"
    - "semantic relation classification via convolutional neural networks with simple negative sampling"
    - "a dependency based neural network for relation classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "a novel hierarchical binary tagging framework for joint extraction of entities and relations"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "cotype joint extraction of typed entities and relations with knowledge bases"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "knowledge base relation detection via multi view matching"):
  with members:
    - "improved neural relation detection for knowledge base question answering"
    - "bilateral multi perspective matching for natural language sentences"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "embedding lexical features via low rank tensors"
    - "text understanding with the attention sum reader network"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "gated attention readers for text comprehension"

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)