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: "towards deep conversational recommendations"
  Abstract: There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

Connected through hyperedge HE-1 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems"):
  with members:
    - "deep conversational recommender in travel"
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "toward building conversational recommender systems a contextual bandit approach"
    - "learning contextual bandits in a non stationary environment"
    - "collaborative memory network for recommendation systems"
    - "explicit state tracking with semi supervisionfor neural dialogue generation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "conversational recommender system"

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"
    - "target guided open domain conversation"
    - "analogical reasoning on chinese morphological and semantic relations"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "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 "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"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"

Connected through hyperedge HE-4 (induced by source paper "target guided open domain conversation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "emergent communication through negotiation"

Connected through hyperedge HE-5 (induced by source paper "deep conversational recommender in travel"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "conversational recommender system"
    - "a knowledge grounded neural conversation model"
    - "latent intention dialogue models"
    - "neural collaborative filtering"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-6 (induced by source paper "towards knowledge based recommender dialog system"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "deep conversational recommender in travel"
    - "graph convolutional neural networks for web scale recommender systems"
    - "generating wikipedia by summarizing long sequences"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-7 (induced by source paper "recommendation as a communication game self supervised bot play for goal oriented dialogue"):
  with members:
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "starspace embed all the things"
    - "neural collaborative filtering"
    - "hierarchical text generation and planning for strategic dialogue"

Connected through hyperedge HE-8 (induced by source paper "proactive human machine conversation with explicit conversation goals"):
  with members:
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards exploiting background knowledge for building conversation systems"
    - "a knowledge grounded neural conversation model"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

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)