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: "improving slot filling performance with attentive neural networks on dependency structures"
  Abstract: Slot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities\_of\_residence) for a given entity from a large collection of source documents. In this paper we propose an effective DNN architecture for SF with the following new strategies: (1). Take a regularized dependency graph instead of a raw sentence as input to DNN, to compress the wide contexts between query and candidate filler; (2). Incorporate two attention mechanisms: local attention learned from query and candidate filler, and global attention learned from external knowledge bases, to guide the model to better select indicative contexts to determine slot type. Experiments show that this framework outperforms state-of-the-art on both relation extraction (16\% absolute F-score gain) and slot filling validation for each individual system (up to 8.5\% absolute F-score gain).

Connected through hyperedge HE-1 (induced by source paper "effective attention modeling for neural relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "improving distantly supervised relation extraction with neural noise converter and conditional optimal selector"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "attention is all you need"
    - "bidirectional attention flow for machine comprehension"

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"
    - "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"
    - "combining recurrent and convolutional neural networks for relation classification"

Connected through hyperedge HE-3 (induced by source paper "handling divergent reference texts when evaluating table to text generation"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "towards a better metric for evaluating question generation systems"
    - "object hallucination in image captioning"
    - "identifying semantic divergences in parallel text without annotations"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-4 (induced by source paper "joint learning of word and label embeddings for sequence labelling in spoken language understanding"):
  with members:
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "joint embedding of words and labels for text classification"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "knowledge as a teacher knowledge guided structural attention networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural machine translation by jointly learning to align and translate"

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)