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: "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
  Abstract: Reliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language Processing. In this paper, we present a system for relation classification and extraction based on an ensemble of convolutional and recurrent neural networks that ranked first in 3 out of the 4 subtasks at SemEval 2018 Task 7. We provide detailed explanations and grounds for the design choices behind the most relevant features and analyze their importance.

Connected through hyperedge HE-1 (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"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep contextualized word representations"
    - "a hybrid deep learning approach for medical 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"
    - "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"
    - "n ary relation extraction using graph state lstm"

Connected through hyperedge HE-3 (induced by source paper "advancing nlp with cognitive language processing signals"):
  with members:
    - "entity recognition at first sight improving ner with eye movement information"
    - "finding syntax in human encephalography with beam search"
    - "replicability analysis for natural language processing testing significance with multiple datasets"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "leveraging cognitive features for sentiment analysis"
    - "what to do about non standard or non canonical language in nlp"
    - "improving sentence compression by learning to predict gaze"

Connected through hyperedge HE-4 (induced by source paper "quantitative overfitting management for human in the loop ml application development with ease ml meter"):
  with members:
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "deep contextualized word representations"
    - "patient risk assessment and warning symptom detection using deep attention based neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "snorkel rapid training data creation with weak supervision"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "mllib machine learning in apache spark"
    - "preventing false discovery in interactive data analysis is hard"

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)