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: "improving background based conversation with context aware knowledge pre selection"
  Abstract: Background Based Conversations (BBCs) have been developed to make dialogue systems generate more informative and natural responses by leveraging background knowledge. Existing methods for BBCs can be grouped into two categories: extraction-based methods and generation-based methods. The former extract spans frombackground material as responses that are not necessarily natural. The latter generate responses thatare natural but not necessarily effective in leveraging background knowledge. In this paper, we focus on generation-based methods and propose a model, namely Context-aware Knowledge Pre-selection (CaKe), which introduces a pre-selection process that uses dynamic bi-directional attention to improve knowledge selection by using the utterance history context as prior information to select the most relevant background material. Experimental results show that our model is superior to current state-of-the-art baselines, indicating that it benefits from the pre-selection process, thus improving in-formativeness and fluency.

Connected through hyperedge HE-1 (induced by source paper "refnet a reference aware network for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "challenges in building intelligent open domain dialog systems"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "first principle study on honeycomb fluorated inte monolayer with large rashba spin splitting and direct bandgap"
    - "learning to select knowledge for response generation in dialog systems"
    - "knowledge aware conversation generation with reasoning on augmented graph"

Connected through hyperedge HE-2 (induced by source paper "thinking globally acting locally distantly supervised global to local knowledge selection for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "refnet a reference aware network for background based conversation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "learning to select knowledge for response generation in dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a dataset for document grounded conversations"

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)