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: "comparing convolutional neural networks to traditional models for slot filling"
  Abstract: We address relation classification in the context of slot filling, the task of finding and evaluating fillers like "Steve Jobs" for the slot X in "X founded Apple". We propose a convolutional neural network which splits the input sentence into three parts according to the relation arguments and compare it to state-of-the-art and traditional approaches of relation classification. Finally, we combine different methods and show that the combination is better than individual approaches. We also analyze the effect of genre differences on performance.

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"
    - "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-2 (induced by source paper "type aware convolutional neural networks for slot filling"):
  with members:
    - "cis at tac cold start 2015 neural networks and coreference resolution for slot filling"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "improving slot filling performance with attentive neural networks on dependency structures"
    - "neural architectures for named entity recognition"
    - "noise mitigation for neural entity typing and relation extraction"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-3 (induced by source paper "deep learning applied to nlp"):
  with members:
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "dependency based convolutional neural networks for sentence embedding"
    - "a re ranking model for dependency parser with recursive convolutional neural network"
    - "modelling visualising and summarising documents with a single convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "knowledge base population using semantic label propagation"):
  with members:
    - "semantic relation classification via convolutional neural networks with simple negative sampling"
    - "incremental knowledge base construction using deepdive"
    - "efficient estimation of word representations in vector space"

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)