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: "classifying relations by ranking with convolutional neural networks"
  Abstract: Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

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 "improving relation extraction with knowledge attention"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "position aware self attention with relative positional encodings for slot filling"
    - "on2vec embedding based relation prediction for ontology population"
    - "multi level structured self attentions for distantly supervised relation extraction"
    - "improved neural relation detection for knowledge base question answering"
    - "neural natural language inference models enhanced with external knowledge"

Connected through hyperedge HE-4 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "characterizing question facets for complex answer retrieval"
    - "structuring wikipedia articles with section recommendations"
    - "text segmentation as a supervised learning task"
    - "do convolutional networks need to be deep for text classification"
    - "benchmark for complex answer retrieval"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-5 (induced by source paper "improving neural relation extraction with implicit mutual relations"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "predicting semantic relations using global graph properties"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reinforcement learning for relation classification from noisy data"

Connected through hyperedge HE-6 (induced by source paper "docred a large scale document level relation extraction dataset"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "improving the transformer translation model with document level context"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "fever a large scale dataset for fact extraction and verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "neural architectures for open type relation argument extraction"):
  with members:
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "zero shot relation extraction via reading comprehension"
    - "reading wikipedia to answer open domain questions"
    - "pointing the unknown words"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "squad 100 000 questions for machine comprehension of text"
    - "cotype joint extraction of typed entities and relations with knowledge bases"

Connected through hyperedge HE-8 (induced by source paper "deep ranking based cost sensitive multi label learning for distant supervision relation extraction"):
  with members:
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "dsgan generative adversarial training for distant supervision relation extraction"
    - "robust distant supervision relation extraction via deep reinforcement learning"
    - "semi supervised learning for neural keyphrase generation"
    - "encoding implicit relation requirements for relation extraction a joint inference approach"
    - "reinforcement learning for relation classification from noisy data"
    - "denoising distant supervision for relation extraction via instance level adversarial training"

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)