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: "effective slot filling based on shallow distant supervision methods"
  Abstract: Spoken Language Systems at Saarland University (LSV) participated this year with 5 runs at the TAC KBP English slot filling track. Effective algorithms for all parts of the pipeline, from document retrieval to relation prediction and response post-processing, are bundled in a modular end-to-end relation extraction system called RelationFactory. The main run solely focuses on shallow techniques and achieved significant improvements over LSV's last year's system, while using the same training data and patterns. Improvements mainly have been obtained by a feature representation focusing on surface skip n-grams and improved scoring for extracted distant supervision patterns. Important factors for effective extraction are the training and tuning scheme for distant supervision classifiers, and the query expansion by a translation model based on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the submitted main run of the LSV RelationFactory system achieved the top-ranked F1-score of 37.3%.

Connected through hyperedge HE-1 (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"
    - "comparing convolutional neural networks to traditional models for slot filling"
    - "noise mitigation for neural entity typing and relation extraction"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-2 (induced by source paper "nero a neural rule grounding framework for label efficient relation extraction"):
  with members:
    - "learning dual retrieval module for semi supervised relation extraction"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "metapad meta pattern discovery from massive text corpora"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-3 (induced by source paper "neural rule grounding for low resource relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "generalize symbolic knowledge with neural rule engine"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "joint bootstrapping machines for high confidence relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "metapad meta pattern discovery from massive text corpora"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-4 (induced by source paper "comparing convolutional neural networks to traditional models for slot filling"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "classifying relations by ranking with convolutional neural networks"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

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)