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: "temporal information extraction for question answering using syntactic dependencies in an lstm based architecture"
  Abstract: In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A "double-checking" technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "joint event and temporal relation extraction with shared representations and structured prediction"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a structured learning approach to temporal relation extraction"
    - "cogcomptime a tool for understanding time in natural language text"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi axis annotation scheme for event temporal relations"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-2 (induced by source paper "deep structured neural network for event temporal relation extraction"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "contextualized word embeddings enhanced event temporal relation extraction for story understanding"
    - "a structured learning approach to temporal relation extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi axis annotation scheme for event temporal relations"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"

Connected through hyperedge HE-3 (induced by source paper "embedding time expressions for deep temporal ordering models"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a structured learning approach to temporal relation extraction"
    - "deep contextualized word representations"
    - "a multi axis annotation scheme for event temporal relations"
    - "improving temporal relation extraction with a globally acquired statistical resource"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "contextualized word embeddings enhanced event temporal relation extraction for story understanding"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi axis annotation scheme for event temporal relations"
    - "attention is all you need"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"

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.IR(Information Retrieval)