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: "natural language generation enhances human decision making with uncertain information"
  Abstract: Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentations for uncertain data and, for the first time, measure their effects on human decision-making. We show that the use of Natural Language Generation (NLG) improves decision-making under uncertainty, compared to state-of-the-art graphical-based representation methods. In a task-based study with 442 adults, we found that presentations using NLG lead to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations).

Connected through hyperedge HE-1 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-3 (induced by source paper "challenges in collaborative hri for remote robot teams"):
  with members:
    - "the orca hub explainable offshore robotics through intelligent interfaces"
    - "playing pairs with pepper"
    - "miriam a multimodal chat based interface for autonomous systems"

Connected through hyperedge HE-4 (induced by source paper "rankme reliable human ratings for natural language generation"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "referenceless quality estimation for natural language generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "challenges in data to document generation"
    - "the e2e dataset new challenges for end to end generation"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"
    - "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking"

Connected through hyperedge HE-5 (induced by source paper "tell me why is it so explaining knowledge graph relationships by finding descriptive support passages"):
  with members:
    - "timemachine timeline generation for knowledge base entities"
    - "rex explaining relationships between entity pairs"

Connected through hyperedge HE-6 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

Connected through hyperedge HE-7 (induced by source paper "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"):
  with members:
    - "referenceless quality estimation for natural language generation"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "the e2e dataset new challenges for end to end generation"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "deep reinforcement learning for multi domain dialogue systems"
    - "natural language generation in dialogue using lexicalized and delexicalized data"

Connected through hyperedge HE-8 (induced by source paper "ld sds towards an expressive spoken dialogue system based on linked data"):
  with members:
    - "a frame tracking model for memory enhanced dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "language generation with recurrent generative adversarial networks without pre training"
    - "a network based end to end trainable task oriented dialogue system"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural text generation from structured data with application to the biography domain"
    - "dialogue manager domain adaptation using gaussian process reinforcement learning"
    - "end to end joint learning of natural language understanding and dialogue manager"

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)