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: "entropy computations of document images in run length compressed domain"
  Abstract: Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].

Connected through hyperedge HE-1 (induced by source paper "direct processing of document images in compressed domain"):
  with members:
    - "automatic detection of font size straight from run length compressed text documents"
    - "direct processing of run length compressed document image for segmentation and characterization of a specified block"
    - "extraction of line word character segments directly from run length compressed printed text documents"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

Connected through hyperedge HE-2 (induced by source paper "automatic detection of font size straight from run length compressed text documents"):
  with members:
    - "direct processing of run length compressed document image for segmentation and characterization of a specified block"
    - "extraction of line word character segments directly from run length compressed printed text documents"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

Connected through hyperedge HE-3 (induced by source paper "direct processing of run length compressed document image for segmentation and characterization of a specified block"):
  with members:
    - "extraction of line word character segments directly from run length compressed printed text documents"
    - "extraction of projection profile run histogram and entropy features straight from run length compressed text documents"

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)