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: "attentive memory networks efficient machine reading for conversational search"
  Abstract: Recent advances in conversational systems have changed the search paradigm. Traditionally, a user poses a query to a search engine that returns an answer based on its index, possibly leveraging external knowledge bases and conditioning the response on earlier interactions in the search session. In a natural conversation, there is an additional source of information to take into account: utterances produced earlier in a conversation can also be referred to and a conversational IR system has to keep track of information conveyed by the user during the conversation, even if it is implicit. #R##N#We argue that the process of building a representation of the conversation can be framed as a machine reading task, where an automated system is presented with a number of statements about which it should answer questions. The questions should be answered solely by referring to the statements provided, without consulting external knowledge. The time is right for the information retrieval community to embrace this task, both as a stand-alone task and integrated in a broader conversational search setting. #R##N#In this paper, we focus on machine reading as a stand-alone task and present the Attentive Memory Network (AMN), an end-to-end trainable machine reading algorithm. Its key contribution is in efficiency, achieved by having an hierarchical input encoder, iterating over the input only once. Speed is an important requirement in the setting of conversational search, as gaps between conversational turns have a detrimental effect on naturalness. On 20 datasets commonly used for evaluating machine reading algorithms we show that the AMN achieves performance comparable to the state-of-the-art models, while using considerably fewer computations.

Connected through hyperedge HE-1 (induced by source paper "dynamic search optimizing the game of information seeking"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "query tracking for e commerce conversational search a machine comprehension perspective"
    - "an algorithmic perspective on imitation learning"
    - "a reinforcement learning driven translation model for search oriented conversational systems"
    - "neural approaches to conversational ai"
    - "top k off policy correction for a reinforce recommender system"
    - "a click sequence model for web search"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"

Connected through hyperedge HE-2 (induced by source paper "introducing mantis a novel multi domain information seeking dialogues dataset"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a hybrid retrieval generation neural conversation model"
    - "user intent prediction in information seeking conversations"
    - "interpretation of natural language rules in conversational machine reading"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "integrating planning for task completion dialogue policy learning"
    - "subgoal discovery for hierarchical dialogue policy learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "simpleds a simple deep reinforcement learning dialogue system"

Connected through hyperedge HE-4 (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"
    - "conversational recommender system"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "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-5 (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"
    - "conversational recommender system"
    - "iterative relevance feedback for answer passage retrieval with passage level semantic match"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "neural networks for information retrieval"):
  with members:
    - "neural vector spaces for unsupervised information retrieval"
    - "neural ranking models with weak supervision"
    - "reply with proactive recommendation of email attachments"
    - "a neural knowledge language model"
    - "learning to attend copy and generate for session based query suggestion"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "avoiding your teacher s mistakes training neural networks with controlled weak supervision"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-7 (induced by source paper "response ranking with deep matching networks and external knowledge in information seeking conversation systems"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

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)