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: "combining neural networks and log linear models to improve relation extraction"
  Abstract: The last decade has witnessed the success of the traditional feature-based method on exploiting the discrete structures such as words or lexical patterns to extract relations from text. Recently, convolutional and recurrent neural networks has provided very effective mechanisms to capture the hidden structures within sentences via continuous representations, thereby significantly advancing the performance of relation extraction. The advantage of convolutional neural networks is their capacity to generalize the consecutive k-grams in the sentences while recurrent neural networks are effective to encode long ranges of sentence context. This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages. Our systematic evaluation of different network architectures and combination methods demonstrates the effectiveness of this approach and results in the state-of-the-art performance on the ACE 2005 and SemEval dataset.

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 "reflex flexible framework for relation extraction in multiple domains"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "publicly available clinical bert embeddings"
    - "scispacy fast and robust models for biomedical natural language processing"
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "rethinking cooperative rationalization introspective extraction and complement control"):
  with members:
    - "guiding extractive summarization with question answering rewards"
    - "interpretable neural predictions with differentiable binary variables"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "game theoretic interpretability for temporal modeling"
    - "towards robust interpretability with self explaining neural networks"
    - "extractive adversarial networks high recall explanations for identifying personal attacks in social media posts"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "learning to explain an information theoretic perspective on model interpretation"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "transfer learning for relation extraction via relation gated adversarial learning"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "adversarial domain adaptation for duplicate question detection"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "partial adversarial domain adaptation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "see syntax aware entity embedding for neural relation extraction"

Connected through hyperedge HE-7 (induced by source paper "end to end neural relation extraction using deep biaffine attention"):
  with members:
    - "joint entity recognition and relation extraction as a multi head selection problem"
    - "adversarial training for multi context joint entity and relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "deep biaffine attention for neural dependency parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-8 (induced by source paper "on the effectiveness of the pooling methods for biomedical relation extraction with deep learning"):
  with members:
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "convolutional neural networks for chemical disease relation extraction are improved with character based word embeddings"
    - "regularizing and optimizing lstm language models"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "classifying relations by ranking with convolutional neural networks"
    - "a dependency based neural network for relation classification"

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)