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: "memory assisted universal compression of network flows"
  Abstract: Recently, the existence of considerable amount of redundancy in the Internet traffic has stimulated the deployment of several redundancy elimination techniques within the network. These techniques are often based on either packet-level Redundancy Elimination (RE) or Content-Centric Networking (CCN). However, these techniques cannot exploit sub-packet redundancies. Further, other alternative techniques such as the end-to-end universal compression solutions would not perform well either over the Internet traffic, as such techniques require infinite length traffic to effectively remove redundancy. This paper proposes a memory-assisted universal compression technique that holds a significant promise for reducing the amount of traffic in the networks. The proposed work is based on the observation that if a source is to be compressed and sent over a network, the associated universal code entails a substantial overhead in transmission due to finite length traffic. However, intermediate nodes can learn the source statistics and this can be used to reduce the cost of describing the source statistics, reducing the transmission overhead for such traffics. We present two algorithms (statistical and dictionary-based) for the memory-assisted universal lossless compression of information sources. These schemes are universal in the sense that they do not require any prior knowledge of the traffic's statistical distribution. We demonstrate the effectiveness of both algorithms and characterize the memorization gain using the real Internet traces. Furthermore, we apply these compression schemes to Internet-like power-law graphs and solve the routing problem for compressed flows.

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"
    - "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"
    - "on the network wide gain of memory assisted source coding"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "on lossless universal compression of distributed identical sources"):
  with members:
    - "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"
    - "on the network wide gain of memory assisted source coding"

Connected through hyperedge HE-4 (induced by source paper "results on the fundamental gain of memory assisted universal source coding"):
  with members:
    - "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)