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: "target guided open domain conversation"
  Abstract: Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches.

Connected through hyperedge HE-1 (induced by source paper "i love your chain mail making knights smile in a fantasy game world open domain goal orientated dialogue agents"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "hierarchical decision making by generating and following natural language instructions"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "countering language drift via visual grounding"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "learning semantic textual similarity from conversations"
    - "textworld a learning environment for text based games"

Connected through hyperedge HE-2 (induced by source paper "dynamic knowledge routing network for target guided open domain conversation"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "end to end knowledge routed relational dialogue system for automatic diagnosis"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "towards deep conversational recommendations"
    - "emergent communication through negotiation"
    - "conversational ai the science behind the alexa prize"
    - "dailydialog a manually labelled multi turn dialogue dataset"

Connected through hyperedge HE-3 (induced by source paper "conversation generation with concept flow"):
  with members:
    - "the curious case of neural text degeneration"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "neural text generation with unlikelihood training"
    - "deep contextualized word representations"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "a dataset for document grounded conversations"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "sounding board a user centric and content driven social chatbot"

Connected through hyperedge HE-4 (induced by source paper "texar a modularized versatile and extensible toolkit for text generation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "deepmind control suite"
    - "unsupervised text style transfer using language models as discriminators"
    - "tensor2tensor for neural machine translation"
    - "deep generative models with learnable knowledge constraints"
    - "xnmt the extensible neural machine translation toolkit"
    - "marian fast neural machine translation in c"
    - "hybrid retrieval generation reinforced agent for medical image report 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)