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: "alime assist an intelligent assistant for creating an innovative e commerce experience"
  Abstract: We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.

Connected through hyperedge HE-1 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems"):
  with members:
    - "a hybrid retrieval generation neural conversation model"
    - "attentive history selection for conversational question answering"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "attentive history selection for conversational question answering"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "answer interaction in non factoid question answering systems"
    - "multi task deep neural networks for natural language understanding"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "analyzing and characterizing user intent in information seeking conversations"

Connected through hyperedge HE-3 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "learning a matching model with co teaching for multi turn response selection in retrieval based dialogue systems"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "co sampling training robust networks for extremely noisy supervision"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "a sequential matching framework for multi turn response selection in retrieval based chatbots"
    - "fidelity weighted learning"
    - "a persona based neural conversation model"

Connected through hyperedge HE-5 (induced by source paper "enriching conversation context in retrieval based chatbots"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "asking clarifying questions in open domain information seeking conversations"
    - "multi task deep neural networks for natural language understanding"
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "training millions of personalized dialogue agents"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "neural approaches to conversational ai"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"

Connected through hyperedge HE-6 (induced by source paper "alime kbqa question answering over structured knowledge for e commerce customer service"):
  with members:
    - "semantic parsing with syntax and table aware sql generation"
    - "a survey on open information extraction"
    - "convolutional neural networks for sentence classification"
    - "question answering with subgraph embeddings"

Connected through hyperedge HE-7 (induced by source paper "improving matching models with hierarchical contextualized representations for multi turn response selection"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "hierarchical recurrent attention network for response generation"
    - "semi supervised sequence tagging with bidirectional language models"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-8 (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"
    - "attentive memory networks efficient machine reading for conversational search"
    - "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)