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: "tempeval 3 evaluating events time expressions and temporal relations"
  Abstract: We describe the TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on temporal information processing. TempEval-3 follows on from previous TempEval events, incorporating: a three-part task structure covering event, temporal expression and temporal relation extraction; a larger dataset; and single overall task quality scores.

Connected through hyperedge HE-1 (induced by source paper "narrativetime dense high speed temporal annotation on a timeline"):
  with members:
    - "a multi axis annotation scheme for event temporal relations"

Connected through hyperedge HE-2 (induced by source paper "word level loss extensions for neural temporal relation classification"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "relation classification via recurrent neural network"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "a universal part of speech tagset"
    - "a model of inductive bias learning"

Connected through hyperedge HE-3 (induced by source paper "optimal hyperparameters for deep lstm networks for sequence labeling tasks"):
  with members:
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "learning word relatedness over time"):
  with members:
    - "using word embeddings for automatic query expansion"
    - "query expansion with locally trained word embeddings"
    - "history by diversity helping historians search news archives"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-5 (induced by source paper "a sequential model for classifying temporal relations between intra sentence events"):
  with members:
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "using signals to improve automatic classification of temporal relations"

Connected through hyperedge HE-6 (induced by source paper "temporal information extraction for question answering using syntactic dependencies in an lstm based architecture"):
  with members:
    - "classifying relations via long short term memory networks along shortest dependency path"

Connected through hyperedge HE-7 (induced by source paper "extracting temporal and causal relations between events"):
  with members:
    - "from frequency to meaning vector space models of semantics"
    - "similarity of semantic relations"

Connected through hyperedge HE-8 (induced by source paper "detecting and extracting events from text documents"):
  with members:
    - "using a probabilistic class based lexicon for lexical ambiguity resolution"

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)