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: "does a plane imitate a bird does computer vision have to follow biological paradigms"
  Abstract: We posit a new paradigm for image information processing. For the last 25 years, this task was usually approached in the frame of Treisman's two-stage paradigm [1]. The latter supposes an unsupervised, bottom-up directed process of preliminary information pieces gathering at the lower processing stages and a supervised, top-down directed process of information pieces binding and grouping at the higher stages. It is acknowledged that these sub-processes interact and intervene between them in a tricky and a complicated manner. Notwithstanding the prevalence of this paradigm in biological and computer vision, we nevertheless propose to replace it with a new one, which we would like to designate as a two-part paradigm. In it, information contained in an image is initially extracted in an independent top-down manner by one part of the system, and then it is examined and interpreted by another, separate system part. We argue that the new paradigm seems to be more plausible than its forerunner. We provide evidence from human attention vision studies and insights of Kolmogorov's complexity theory to support our arguments. We also provide some reasons in favor of separate image interpretation issues.

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"
    - "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"
    - "searching for image information content its discovery extraction and representation"

Connected through hyperedge HE-2 (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"
    - "searching for image information content its discovery extraction and representation"

Connected through hyperedge HE-3 (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"
    - "searching for image information content its discovery extraction and representation"

Connected through hyperedge HE-4 (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"
    - "searching for image information content its discovery extraction and representation"

Connected through hyperedge HE-5 (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"
    - "searching for image information content its discovery extraction and representation"

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.NE(Neural and Evolutionary Computing)