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 temporal relation extraction with a globally acquired statistical resource"
  Abstract: Extracting temporal relations (before, after, overlapping, etc.) is a key aspect of understanding events described in natural language. We argue that this task would gain from the availability of a resource that provides prior knowledge in the form of the temporal order that events usually follow. This paper develops such a resource -- a probabilistic knowledge base acquired in the news domain -- by extracting temporal relations between events from the New York Times (NYT) articles over a 20-year span (1987--2007). We show that existing temporal extraction systems can be improved via this resource. As a byproduct, we also show that interesting statistics can be retrieved from this resource, which can potentially benefit other time-aware tasks. The proposed system and resource are both publicly available.

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"):
  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"
    - "roberta a robustly optimized bert pretraining approach"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "deep contextualized word representations"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "an improved neural baseline for temporal relation extraction"):
  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"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi axis annotation scheme for event temporal relations"
    - "temporal information extraction by predicting relative time lines"

Connected through hyperedge HE-4 (induced by source paper "how large are lions inducing distributions over quantitative attributes"):
  with members:
    - "numeracy for language models evaluating and improving their ability to predict numbers"
    - "robust handling of polysemy via sparse representations"
    - "verb physics relative physical knowledge of actions and objects"
    - "how much is 131 million dollars putting numbers in perspective with compositional descriptions"
    - "globally normalized transition based neural networks"
    - "numerically grounded language models for semantic error correction"
    - "are elephants bigger than butterflies reasoning about sizes of objects"

Connected through hyperedge HE-5 (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"
    - "temporal information extraction for question answering using syntactic dependencies in an lstm based architecture"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "cogcomptime a tool for understanding time in natural language text"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a structured learning approach to temporal relation extraction"
    - "a multi axis annotation scheme for event temporal relations"
    - "the use of classifiers in sequential inference"

Connected through hyperedge HE-7 (induced by source paper "joint reasoning for temporal and causal relations"):
  with members:
    - "a structured learning approach to temporal relation extraction"
    - "a multi axis annotation scheme for event temporal relations"

Connected through hyperedge HE-8 (induced by source paper "neural ranking models for temporal dependency structure parsing"):
  with members:
    - "structured interpretation of temporal relations"
    - "a multi axis annotation scheme for event temporal relations"
    - "end to end neural coreference resolution"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "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.AI(Artificial Intelligence)