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: "results on the fundamental gain of memory assisted universal source coding"
  Abstract: Many applications require data processing to be performed on individual pieces of data which are of finite sizes, e.g., files in cloud storage units and packets in data networks. However, traditional universal compression solutions would not perform well over the finite-length sequences. Recently, we proposed a framework called memory-assisted universal compression that holds a significant promise for reducing the amount of redundant data from the finite-length sequences. The proposed compression scheme is based on the observation that it is possible to learn source statistics (by memorizing previous sequences from the source) at some intermediate entities and then leverage the memorized context to reduce redundancy of the universal compression of finite-length sequences. We first present the fundamental gain of the proposed memory-assisted universal source coding over conventional universal compression (without memorization) for a single parametric source. Then, we extend and investigate the benefits of the memory-assisted universal source coding when the data sequences are generated by a compound source which is a mixture of parametric sources. We further develop a clustering technique within the memory-assisted compression framework to better utilize the memory by classifying the observed data sequences from a mixture of parametric sources. Finally, we demonstrate through computer simulations that the proposed joint memorization and clustering technique can achieve up to 6-fold improvement over the traditional universal compression technique when a mixture of non-binary Markov sources is considered.

Connected through hyperedge HE-1 (induced by source paper "universal compression with side information from a correlated source"):
  with members:
    - "a characterization of guesswork on swiftly tilting curves"
    - "asymptotics and non asymptotics for universal fixed to variable source coding"
    - "packet level network compression realization and scaling of the network wide benefits"
    - "results on the redundancy of universal compression for finite length sequences"

Connected through hyperedge HE-2 (induced by source paper "packet level network compression realization and scaling of the network wide benefits"):
  with members:
    - "a universal parallel two pass mdl context tree compression algorithm"
    - "memory assisted universal compression of network flows"
    - "mixing strategies in data compression"
    - "results on the redundancy of universal compression for finite length sequences"
    - "on the network wide gain of memory assisted source coding"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-3 (induced by source paper "universal compression of a mixture of parametric sources with side information"):
  with members:
    - "a universal parallel two pass mdl context tree compression algorithm"
    - "a parallel two pass mdl context tree algorithm for universal source coding"
    - "packet level network compression realization and scaling of the network wide benefits"
    - "on lossless universal compression of distributed identical sources"
    - "on the network wide gain of memory assisted source coding"

Connected through hyperedge HE-4 (induced by source paper "network compression memory assisted universal coding of sources with correlated parameters"):
  with members:
    - "on lossless universal compression of distributed identical sources"
    - "memory assisted universal compression of network flows"
    - "memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"
    - "on the network wide gain of memory assisted source coding"
    - "femtocell networks a survey"
    - "uplink capacity and interference avoidance for two tier femtocell networks"

Connected through hyperedge HE-5 (induced by source paper "on lossless universal compression of distributed identical sources"):
  with members:
    - "memory assisted universal compression of network flows"
    - "memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"
    - "on the network wide gain of memory assisted source coding"

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.IT(Information Theory)