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: "automatic extraction of causal relations from natural language texts a comprehensive survey"
  Abstract: Automatic extraction of cause-effect relationships from natural language texts is a challenging open problem in Artificial Intelligence. Most of the early attempts at its solution used manually constructed linguistic and syntactic rules on small and domain-specific data sets. However, with the advent of big data, the availability of affordable computing power and the recent popularization of machine learning, the paradigm to tackle this problem has slowly shifted. Machines are now expected to learn generic causal extraction rules from labelled data with minimal supervision, in a domain independent-manner. In this paper, we provide a comprehensive survey of causal relation extraction techniques from both paradigms, and analyse their relative strengths and weaknesses, with recommendations for future work.

Connected through hyperedge HE-1 (induced by source paper "directions for explainable knowledge enabled systems"):
  with members:
    - "explaining explanations in ai"
    - "explaining explanations an overview of interpretability of machine learning"
    - "blockchain and artificial intelligence"
    - "explanation in artificial intelligence insights from the social sciences"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "accountability of ai under the law the role of explanation"
    - "the blockchain a new framework for robotic swarm systems"
    - "the mythos of model interpretability"

Connected through hyperedge HE-2 (induced by source paper "a multi level neural network for implicit causality detection in web texts"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "using natural language processing to extract health related causality from twitter messages"):
  with members:
    - "how do you relax when you re stressed a content analysis and infodemiology study of stress related tweets"
    - "enhancing twitter data analysis with simple semantic filtering example in tracking influenza like illnesses"
    - "syndromic classification of twitter messages"
    - "omg u got flu analysis of shared health messages for bio surveillance"

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)