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: "creating causal embeddings for question answering with minimal supervision"
  Abstract: A common model for question answering (QA) is that a good answer is one that is closely related to the question, where relatedness is often determined using general-purpose lexical models such as word embeddings. We argue that a better approach is to look for answers that are related to the question in a relevant way, according to the information need of the question, which may be determined through task-specific embeddings. With causality as a use case, we implement this insight in three steps. First, we generate causal embeddings cost-effectively by bootstrapping cause-effect pairs extracted from free text using a small set of seed patterns. Second, we train dedicated embeddings over this data, by using task-specific contexts, i.e., the context of a cause is its effect. Finally, we extend a state-of-the-art reranking approach for QA to incorporate these causal embeddings. We evaluate the causal embedding models both directly with a casual implication task, and indirectly, in a downstream causal QA task using data from Yahoo! Answers. We show that explicitly modeling causality improves performance in both tasks. In the QA task our best model achieves 37.3% P@1, significantly outperforming a strong baseline by 7.7% (relative).

Connected through hyperedge HE-1 (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"
    - "a structured learning approach to temporal relation extraction"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "learning to answer subjective specific product related queries using customer reviews by adversarial domain adaptation"):
  with members:
    - "adversarial domain adaptation for machine reading comprehension"
    - "stochastic answer networks for machine reading comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "ms marco a human generated machine reading comprehension dataset"
    - "modeling ambiguity subjectivity and diverging viewpoints in opinion question answering systems"
    - "a neural attention model for abstractive sentence summarization"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "addressing complex and subjective product related queries with customer reviews"

Connected through hyperedge HE-3 (induced by source paper "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"):
  with members:
    - "expanding subjective lexicons for social media mining with embedding subspaces"
    - "improving reliability of word similarity evaluation by redesigning annotation task and performance measure"
    - "linear algebraic structure of word senses with applications to polysemy"
    - "non distributional word vector representations"
    - "big data small data in domain out of domain known word unknown word the impact of word representation on sequence labelling tasks"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "a primer on neural network models for natural language processing"
    - "wordrep a benchmark for research on learning word representations"

Connected through hyperedge HE-4 (induced by source paper "detecting and explaining causes from text for a time series event"):
  with members:
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "a decomposable attention model for natural language inference"
    - "enhanced lstm for natural language inference"
    - "generating natural language inference chains"
    - "reasoning about entailment with neural attention"
    - "a large annotated corpus for learning natural language inference"
    - "neural machine translation by jointly learning to align and translate"
    - "non negative matrix factorization with sparseness constraints"

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)