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 structured learning approach to temporal relation extraction"
  Abstract: Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.

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"
    - "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"
    - "reasoning about actions and state changes by injecting commonsense knowledge"

Connected through hyperedge HE-2 (induced by source paper "everything happens for a reason discovering the purpose of actions in procedural text"):
  with members:
    - "tracking discrete and continuous entity state for process understanding"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "what happened leveraging verbnet to predict the effects of actions in procedural text"
    - "simulating action dynamics with neural process networks"
    - "a neural knowledge language model"

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"
    - "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"
    - "improving temporal relation extraction with a globally acquired statistical resource"

Connected through hyperedge HE-4 (induced by source paper "fine grained temporal relation extraction"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "decomposing generalization models of generic habitual and episodic statements"
    - "deep contextualized word representations"
    - "neural models of factuality"
    - "lexicosyntactic inference in neural models"
    - "temporal information extraction by predicting relative time lines"
    - "attention is all you need"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "wiqa a dataset for what if reasoning over procedural text"):
  with members:
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "simulating action dynamics with neural process networks"
    - "a decomposable attention model for natural language inference"
    - "tracking the world state with recurrent entity networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-7 (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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a multi axis annotation scheme for event temporal relations"
    - "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 "embedding time expressions for deep temporal ordering models"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "deep contextualized word representations"
    - "a multi axis annotation scheme for event temporal relations"
    - "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"

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)