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: "interactive spoken content retrieval by deep reinforcement learning"
  Abstract: User-machine interaction is important for spoken content retrieval. For text content retrieval, the user can easily scan through and select on a list of retrieved item. This is impossible for spoken content retrieval, because the retrieved items are difficult to show on screen. Besides, due to the high degree of uncertainty for speech recognition, the retrieval results can be very noisy. One way to counter such difficulties is through user-machine interaction. The machine can take different actions to interact with the user to obtain better retrieval results before showing to the user. The suitable actions depend on the retrieval status, for example requesting for extra information from the user, returning a list of topics for user to select, etc. In our previous work, some hand-crafted states estimated from the present retrieval results are used to determine the proper actions. In this paper, we propose to use Deep-Q-Learning techniques instead to determine the machine actions for interactive spoken content retrieval. Deep-Q-Learning bypasses the need for estimation of the hand-crafted states, and directly determine the best action base on the present retrieval status even without any human knowledge. It is shown to achieve significantly better performance compared with the previous hand-crafted states.

Connected through hyperedge HE-1 (induced by source paper "analysis of evolutionary behavior in self learning media search engines"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "optimizing query evaluations using reinforcement learning for web search"
    - "sketch based manga retrieval using manga109 dataset"
    - "cross media similarity evaluation for web image retrieval in the wild"

Connected through hyperedge HE-2 (induced by source paper "joint learning of interactive spoken content retrieval and trainable user simulator"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "a user simulator for task completion dialogues"
    - "a sequence to sequence model for user simulation in spoken dialogue systems"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

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)