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: "dialog based language learning"
  Abstract: A long-term goal of machine learning research is to build an intelligent dialog agent. Most research in natural language understanding has focused on learning from fixed training sets of labeled data, with supervision either at the word level (tagging, parsing tasks) or sentence level (question answering, machine translation). This kind of supervision is not realistic of how humans learn, where language is both learned by, and used for, communication. In this work, we study dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. We study this setup in two domains: the bAbI dataset of (Weston et al., 2015) and large-scale question answering from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all.

Connected through hyperedge HE-1 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification"):
  with members:
    - "learning from dialogue after deployment feed yourself chatbot"
    - "a scalable neural shortlisting reranking approach for large scale domain classification in natural language understanding"
    - "efficient large scale domain classification with personalized attention"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "classification from positive unlabeled and biased negative data"
    - "supervised domain enablement attention for personalized domain classification"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-2 (induced by source paper "masking orchestration multi task pretraining for multi role dialogue representation learning"):
  with members:
    - "pretraining methods for dialog context representation learning"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "language model pre training for hierarchical document representations"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "dialogue act recognition via crf attentive structured network"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-4 (induced by source paper "learning from dialogue after deployment feed yourself chatbot"):
  with members:
    - "beyond turing intelligent agents centered on the user"
    - "bootstrapping conversational agents with weak supervision"
    - "training millions of personalized dialogue agents"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "interactive language acquisition with one shot visual concept learning through a conversational game"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "grounding human to vehicle advice for self driving vehicles"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "textual explanations for self driving vehicles"
    - "reward learning from narrated demonstrations"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "teaching machines to describe images via natural language feedback"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-6 (induced by source paper "antnet deep answer understanding network for natural reverse qa"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "listen interact and talk learning to speak via interaction"):
  with members:
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "third person imitation learning"
    - "adversarial learning for neural dialogue generation"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "learning to learn by gradient descent by gradient descent"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-8 (induced by source paper "interactive language acquisition with one shot visual concept learning through a conversational game"):
  with members:
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "third person imitation learning"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "learning language games through interaction"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"

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)