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: "optimizing dialogue management with reinforcement learning experiments with the njfun system"
  Abstract: Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.

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"
    - "target guided open domain conversation"
    - "learning semantic textual similarity from conversations"

Connected through hyperedge HE-2 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-3 (induced by source paper "domain transfer in dialogue systems without turn level supervision"):
  with members:
    - "fully statistical neural belief tracking"
    - "towards universal dialogue state tracking"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "scalable multi domain dialogue state tracking"
    - "frames a corpus for adding memory to goal oriented dialogue systems"

Connected through hyperedge HE-4 (induced by source paper "neural assistant joint action prediction response generation and latent knowledge reasoning"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "scalable multi domain dialogue state tracking"
    - "latent intention dialogue models"
    - "attention is all you need"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"

Connected through hyperedge HE-5 (induced by source paper "towards personalized dialog policies for conversational skill discovery"):
  with members:
    - "the technological gap between virtual assistants and recommendation systems"
    - "continuous learning for large scale personalized domain classification"
    - "efficient large scale domain classification with personalized attention"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "attentive memory networks efficient machine reading for conversational search"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "learning robust dialog policies in noisy environments"
    - "improving search through a3c reinforcement learning based conversational agent"

Connected through hyperedge HE-6 (induced by source paper "investigation of error simulation techniques for learning dialog policies for conversational error recovery"):
  with members:
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "just ask building an architecture for extensible self service spoken language understanding"
    - "learning robust dialog policies in noisy environments"
    - "end to end task completion neural dialogue systems"
    - "sim2real view invariant visual servoing by recurrent control"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "pre training in deep reinforcement learning for automatic speech recognition"):
  with members:
    - "direct modelling of speech emotion from raw speech"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "multi task semi supervised adversarial autoencoding for speech emotion"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "emorl continuous acoustic emotion classification using deep reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-8 (induced by source paper "augmenting knowledge through statistical goal oriented human robot dialog"):
  with members:
    - "goal oriented dialogue policy learning from failures"
    - "the strands project long term autonomy in everyday environments"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "cornell spf cornell semantic parsing framework"

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.LG(Machine Learning)