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: "joint reasoning for temporal and causal relations"
  Abstract: Understanding temporal and causal relations between events is a fundamental natural language understanding task. Because a cause must be before its effect in time, temporal and causal relations are closely related and one relation even dictates the other one in many cases. However, limited attention has been paid to studying these two relations jointly. This paper presents a joint inference framework for them using constrained conditional models (CCMs). Specifically, we formulate the joint problem as an integer linear programming (ILP) problem, enforcing constraints inherently in the nature of time and causality. We show that the joint inference framework results in statistically significant improvement in the extraction of both temporal and causal relations from text.

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

Connected through hyperedge HE-2 (induced by source paper "aser a large scale eventuality knowledge graph"):
  with members:
    - "a surprisingly robust trick for the winograd schema challenge"
    - "sp 10k a large scale evaluation set for selectional preference acquisition"
    - "how reasonable are common sense reasoning tasks a case study on the winograd schema challenge and swag"
    - "a simple method for commonsense reasoning"
    - "on the evaluation of common sense reasoning in natural language understanding"
    - "a knowledge hunting framework for common sense reasoning"
    - "deep contextualized word representations"
    - "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:
    - "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"
    - "improving temporal relation extraction with a globally acquired statistical resource"

Connected through hyperedge HE-4 (induced by source paper "a multi level neural network for implicit causality detection in web texts"):
  with members:
    - "working memory networks augmenting memory networks with a relational reasoning module"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "a structured self attentive sentence embedding"
    - "gaussian error linear units gelus"

Connected through hyperedge HE-5 (induced by source paper "partial or complete that s the question"):
  with members:
    - "cogcomptime a tool for understanding time in natural language text"
    - "understanding autoencoders with information theoretic concepts"
    - "exploiting partially annotated data for temporal relation extraction"
    - "a multi axis annotation scheme for event temporal relations"
    - "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-6 (induced by source paper "fine grained temporal relation extraction"):
  with members:
    - "decomposing generalization models of generic habitual and episodic statements"
    - "a structured learning approach to temporal relation extraction"
    - "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-7 (induced by source paper "joint event and temporal relation extraction with shared representations and structured prediction"):
  with members:
    - "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"
    - "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-8 (induced by source paper "deep structured neural network for event temporal relation extraction"):
  with members:
    - "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"
    - "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"

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)