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: "a multi axis annotation scheme for event temporal relations"
  Abstract: Existing temporal relation (TempRel) annotation schemes often have low inter-annotator agreements (IAA) even between experts, suggesting that the current annotation task needs a better definition. This paper proposes a new multi-axis modeling to better capture the temporal structure of events. In addition, we identify that event end-points are a major source of confusion in annotation, so we also propose to annotate TempRels based on start-points only. A pilot expert annotation using the proposed scheme shows significant improvement in IAA from the conventional 60's to 80's (Cohen's Kappa). This better-defined annotation scheme further enables the use of crowdsourcing to alleviate the labor intensity for each annotator. We hope that this work can foster more interesting studies towards event understanding.

Connected through hyperedge HE-1 (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-2 (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"
    - "temporal information extraction by predicting relative time lines"
    - "improving temporal relation extraction with a globally acquired statistical resource"

Connected through hyperedge HE-3 (induced by source paper "partial or complete that s the question"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "cogcomptime a tool for understanding time in natural language text"
    - "understanding autoencoders with information theoretic concepts"
    - "exploiting partially annotated data for temporal relation extraction"
    - "active learning with partial feedback"
    - "structured set matching networks for one shot part labeling"
    - "opening the black box of deep neural networks via information"
    - "crowdsourcing question answer meaning representations"

Connected through hyperedge HE-4 (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"
    - "temporal information extraction for question answering using syntactic dependencies in an lstm based architecture"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-5 (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"
    - "temporal information extraction for question answering using syntactic dependencies in an lstm based architecture"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"

Connected through hyperedge HE-6 (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"
    - "improving temporal relation extraction with a globally acquired statistical resource"
    - "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-7 (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"
    - "attention is all you need"
    - "temporal information extraction for question answering using syntactic dependencies in an lstm based architecture"
    - "a corpus and evaluation framework for deeper understanding of commonsense stories"

Connected through hyperedge HE-8 (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"
    - "improving temporal relation extraction with a globally acquired statistical resource"
    - "the use of classifiers in sequential inference"

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)