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: "in quest of image semantics are we looking for it under the right lamppost"
  Abstract: In the last years we witness a dramatic growth of research focused on semantic image understanding. Indeed, without understanding image content successful accomplishment of any image-processing task is simply incredible. Up to the recent times, the ultimate need for such understanding has been met by the knowledge that a domain expert or a vision system supervisor have contributed to every image-processing application. The advent of the Internet has drastically changed this situation. Internet sources of visual information are diffused and dispersed over the whole Web, so the duty of information content discovery and evaluation must be relegated now to an image understanding agent (a machine or a computer program) capable to perform image content assessment at a remote image location. Development of Content Based Image Retrieval (CBIR) techniques was a right move in a right direction, launched about ten years ago. Unfortunately, very little progress has been made since then. The reason for this can be seen in a rank of long lasting misconceptions that CBIR designers are continuing to adhere to. I hope, my arguments will help them to change their minds.

Connected through hyperedge HE-1 (induced by source paper "machine learning when and where the horses went astray"):
  with members:
    - "unveiling the mystery of visual information processing in human brain"
    - "algorithmic information theory"
    - "commonsense knowledge ontology and ordinary language"
    - "algorithmic information theory a brief non technical guide to the field"
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "searching for image information content its discovery extraction and representation"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-2 (induced by source paper "unveiling the mystery of visual information processing in human brain"):
  with members:
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "searching for image information content its discovery extraction and representation"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-3 (induced by source paper "i m sorry to say but your understanding of image processing fundamentals is absolutely wrong"):
  with members:
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "searching for image information content its discovery extraction and representation"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-4 (induced by source paper "modeling visual information processing in brain a computer vision point of view and approach"):
  with members:
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "searching for image information content its discovery extraction and representation"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

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.CV(Computer Vision and Pattern Recognition)