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: "an ensemble model with ranking for social dialogue"
  Abstract: Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence. This year, the Amazon Alexa Prize challenge was announced for the first time, where real customers get to rate systems developed by leading universities worldwide. The aim of the challenge is to converse "coherently and engagingly with humans on popular topics for 20 minutes". We describe our Alexa Prize system (called 'Alana') consisting of an ensemble of bots, combining rule-based and machine learning systems, and using a contextual ranking mechanism to choose a system response. The ranker was trained on real user feedback received during the competition, where we address the problem of how to train on the noisy and sparse feedback obtained during the competition.

Connected through hyperedge HE-1 (induced by source paper "ensemble based deep reinforcement learning for chatbots"):
  with members:
    - "deep reinforcement learning for chatbots using clustered actions and human likeness rewards"
    - "a data efficient deep learning approach for deployable multimodal social robots"
    - "feudal reinforcement learning for dialogue management in large domains"
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a deep reinforcement learning chatbot short version"

Connected through hyperedge HE-2 (induced by source paper "a crowd based evaluation of abuse response strategies in conversational agents"):
  with members:
    - "rankme reliable human ratings for natural language generation"
    - "ethical challenges in data driven dialogue systems"
    - "a neural conversational model"

Connected through hyperedge HE-3 (induced by source paper "neural response ranking for social conversation a data efficient approach"):
  with members:
    - "avoiding echo responses in a retrieval based conversation system"
    - "billion scale similarity search with gpus"
    - "a practical approach to dialogue response generation in closed domains"
    - "edina building an open domain socialbot with self dialogues"
    - "rubystar a non task oriented mixture model dialog system"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "deep reinforcement learning for dialogue generation"
    - "conversational contextual cues the case of personalization and history for response ranking"

Connected through hyperedge HE-4 (induced by source paper "different but equal comparing user collaboration with digital personal assistants vs teams of expert agents"):
  with members:
    - "a deep reinforcement learning chatbot"

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)