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: "know2look commonsense knowledge for visual search"
  Abstract: With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval.

Connected through hyperedge HE-1 (induced by source paper "story oriented image selection and placement"):
  with members:
    - "vse improving visual semantic embeddings with hard negatives"
    - "single image action recognition using semantic body part actions"
    - "yolo9000 better faster stronger"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "visual relationship detection with language priors"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-2 (induced by source paper "visir visual and semantic image label refinement"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "yolo9000 better faster stronger"
    - "cnn rnn a unified framework for multi label image classification"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "contextual action recognition with r cnn"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "lsda large scale detection through adaptation"

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.IR(Information Retrieval)