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: "on the network wide gain of memory assisted source coding"
  Abstract: Several studies have identified a significant amount of redundancy in the network traffic. For example, it is demonstrated that there is a great amount of redundancy within the content of a server over time. This redundancy can be leveraged to reduce the network flow by the deployment of memory units in the network. The question that arises is whether or not the deployment of memory can result in a fundamental improvement in the performance of the network. In this paper, we answer this question affirmatively by first establishing the fundamental gains of memory-assisted source compression and then applying the technique to a network. Specifically, we investigate the gain of memory-assisted compression in random network graphs consisted of a single source and several randomly selected memory units. We find a threshold value for the number of memories deployed in a random graph and show that if the number of memories exceeds the threshold we observe network-wide reduction in the traffic.

Connected through hyperedge HE-1 (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"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "mixing strategies in data compression"
    - "results on the redundancy of universal compression for finite length sequences"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-2 (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"
    - "results on the fundamental gain of memory assisted universal source coding"

Connected through hyperedge HE-3 (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"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"
    - "femtocell networks a survey"
    - "uplink capacity and interference avoidance for two tier femtocell networks"

Connected through hyperedge HE-4 (induced by source paper "on lossless universal compression of distributed identical sources"):
  with members:
    - "memory assisted universal compression of network flows"
    - "results on the fundamental gain of memory assisted universal source coding"
    - "memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"

Connected through hyperedge HE-5 (induced by source paper "results on the fundamental gain of memory assisted universal source coding"):
  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"

Connected through hyperedge HE-6 (induced by source paper "memory assisted universal compression of network flows"):
  with members:
    - "memory assisted universal source coding"
    - "results on the redundancy of universal compression for finite length sequences"

Connected through hyperedge HE-7 (induced by source paper "memory assisted universal source coding"):
  with members:
    - "results on the redundancy of universal compression for finite length sequences"

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)