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: "conversational recommender system"
  Abstract: A personalized conversational sales agent could have much commercial potential. E-commerce companies such as Amazon, eBay, JD, Alibaba etc. are piloting such kind of agents with their users. However, the research on this topic is very limited and existing solutions are either based on single round adhoc search engine or traditional multi round dialog system. They usually only utilize user inputs in the current session, ignoring users' long term preferences. On the other hand, it is well known that sales conversion rate can be greatly improved based on recommender systems, which learn user preferences based on past purchasing behavior and optimize business oriented metrics such as conversion rate or expected revenue. In this work, we propose to integrate research in dialog systems and recommender systems into a novel and unified deep reinforcement learning framework to build a personalized conversational recommendation agent that optimizes a per session based utility function.

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"
    - "towards deep conversational recommendations"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "recsim a configurable simulation platform for recommender systems"):
  with members:
    - "reinforcement learning when all actions are not always available"
    - "advantage amplification in slowly evolving latent state environments"
    - "challenges of real world reinforcement learning"
    - "toward simulating environments in reinforcement learning based recommendations"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep conversational recommender in travel"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "towards deep conversational recommendations"
    - "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"
    - "a knowledge grounded neural conversation model"
    - "latent intention dialogue models"
    - "neural collaborative filtering"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-4 (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"
    - "towards deep conversational recommendations"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "asking clarifying questions in open domain information seeking conversations"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "the technological gap between virtual assistants and recommendation systems"):
  with members:
    - "online learning for non stationary a b tests"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "measuring semantic coherence of a conversation"
    - "a collaborative ranking model with multiple location based similarities for venue suggestion"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "attentive memory networks efficient machine reading for conversational search"
    - "a network based end to end trainable task oriented 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 "learning to ask question based sequential bayesian product search"):
  with members:
    - "technology assisted reviews finding the last few relevant documents by asking yes no questions to reviewers"
    - "playing 20 question game with policy based reinforcement learning"
    - "attentive long short term preference modeling for personalized product search"
    - "learning to ask knowledge acquisition via 20 questions"
    - "joint modeling and optimization of search and recommendation"
    - "word entity duet representations for document ranking"
    - "learning latent vector spaces for product search"
    - "autonomy and reliability of continuous active learning for technology assisted review"

Connected through hyperedge HE-8 (induced by source paper "conversational product search based on negative feedback"):
  with members:
    - "a study of context dependencies in multi page product search"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "iterative relevance feedback for answer passage retrieval with passage level semantic match"
    - "attentive memory networks efficient machine reading for conversational search"
    - "on application of learning to rank for e commerce search"
    - "learning latent vector spaces for product search"
    - "inferring networks of substitutable and complementary products"
    - "when relevance is not enough promoting visual attractiveness for fashion e commerce"

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.IR(Information Retrieval)