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: "searching for image information content its discovery extraction and representation"
  Abstract: Image information content is known to be a complicated and a controversial problem. We posit a new image information content definition. Following the theory of Solomonoff-Kolmogorov-Chaitin's complexity, we define image information content as a set of descriptions of image data structures. Three levels of such description can be generally distinguished: (1) the global level, where the coarse structure of the entire scene is initially outlined; (2) the intermediate level, where structures of separate, nonoverlapping image regions usually associated with individual scene objects are delineated; and (3) the low-level description, where local image structures observed in a limited and restricted field of view are resolved. A technique for creating such image information content descriptors is developed. Its algorithm is presented and elucidated with some examples, which demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "let us first agree on what the term semantics means an unorthodox approach to an age old debate"):
  with members:
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"

Connected through hyperedge HE-2 (induced by source paper "when you talk about information processing what actually do you have in mind"):
  with members:
    - "let us first agree on what the term semantics means an unorthodox approach to an age old debate"

Connected through hyperedge HE-3 (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"
    - "in quest of image semantics are we looking for it under the right lamppost"
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-4 (induced by source paper "not only a lack of right definitions arguments for a shift in information processing paradigm"):
  with members:
    - "unveiling the mystery of visual information processing in human brain"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-5 (induced by source paper "unveiling the mystery of visual information processing in human brain"):
  with members:
    - "in quest of image semantics are we looking for it under the right lamppost"
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-6 (induced by source paper "i m sorry to say but your understanding of image processing fundamentals is absolutely wrong"):
  with members:
    - "in quest of image semantics are we looking for it under the right lamppost"
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "does a plane imitate a bird does computer vision have to follow biological paradigms"

Connected through hyperedge HE-7 (induced by source paper "modeling visual information processing in brain a computer vision point of view and approach"):
  with members:
    - "in quest of image semantics are we looking for it under the right lamppost"
    - "top down unsupervised image segmentation it sounds like oxymoron but actually it is not"
    - "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)