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: "answer based adversarial training for generating clarification questions"
  Abstract: We present an approach for generating clarification questions with the goal of eliciting new information that would make the given textual context more complete. We propose that modeling hypothetical answers (to clarification questions) as latent variables can guide our approach into generating more useful clarification questions. We develop a Generative Adversarial Network (GAN) where the generator is a sequence-to-sequence model and the discriminator is a utility function that models the value of updating the context with the answer to the clarification question. We evaluate on two datasets, using both automatic metrics and human judgments of usefulness, specificity and relevance, showing that our approach outperforms both a retrieval-based model and ablations that exclude the utility model and the adversarial training.

Connected through hyperedge HE-1 (induced by source paper "asking clarifying questions in open domain information seeking conversations"):
  with members:
    - "investigating the successes and failures of bert for passage re ranking"
    - "a deep look into neural ranking models for information retrieval"
    - "target apps selection towards a unified search framework for mobile devices"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-2 (induced by source paper "antnet deep answer understanding network for natural reverse qa"):
  with members:
    - "semi interactive attention network for answer understanding in reverse qa"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "learning to ask neural question generation for reading comprehension"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "bilateral multi perspective matching for natural language sentences"
    - "cross temporal recurrent networks for ranking question answer pairs"

Connected through hyperedge HE-3 (induced by source paper "learning rhyming constraints using structured adversaries"):
  with members:
    - "discourse aware neural rewards for coherent text generation"
    - "learning to write with cooperative discriminators"
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "neural dynamic programming for musical self similarity"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "sentence simplification with deep reinforcement learning"
    - "deep reinforcement learning for dialogue generation"

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)