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: "neural networks models for entity discovery and linking"
  Abstract: This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.

Connected through hyperedge HE-1 (induced by source paper "a multi task learning approach for named entity recognition using local detection"):
  with members:
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "a fixed size encoding method for variable length sequences with its application to neural network language models"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "a general fofe net framework for simple and effective question answering over knowledge bases"):
  with members:
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "large scale simple question answering with memory networks"
    - "open question answering with weakly supervised embedding models"

Connected through hyperedge HE-3 (induced by source paper "content based news recommendation via shortest entity distance over knowledge graphs"):
  with members:
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "distributed representations of sentences and documents"
    - "personalized news recommendation with context trees"
    - "hetesim a general framework for relevance measure in heterogeneous networks"

Connected through hyperedge HE-4 (induced by source paper "a word complexity lexicon and a neural readability ranking model for lexical simplification"):
  with members:
    - "neural cross lingual entity linking"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-5 (induced by source paper "neural cross lingual entity linking"):
  with members:
    - "one for all towards language independent named entity linking"
    - "entity linking for queries by searching wikipedia sentences"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "long short term memory networks for machine reading"
    - "sentence similarity learning by lexical decomposition and composition"
    - "massively multilingual word embeddings"
    - "multi perspective context matching for machine comprehension"
    - "joint learning of the embedding of words and entities for named entity disambiguation"

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)