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 source coding"
  Abstract: The problem of the universal compression of a sequence from a library of several small to moderate length sequences from similar context arises in many practical scenarios, such as the compression of the storage data and the Internet traffic. In such scenarios, it is often required to compress and decompress every sequence individually. However, the universal compression of the individual sequences suffers from significant redundancy overhead. In this paper, we aim at answering whether or not having a memory unit in the middle can result in a fundamental gain in the universal compression. We present the problem setup in the most basic scenario consisting of a server node $S$, a relay node $R$ (i.e., the memory unit), and a client node $C$. We assume that server $S$ wishes to send the sequence $x^n$ to the client $C$ who has never had any prior communication with the server, and hence, is not capable of memorization of the source context. However, $R$ has previously communicated with $S$ to forward previous sequences from $S$ to the clients other than $C$, and thus, $R$ has memorized a context $y^m$ shared with $S$. Note that if the relay node was absent the source could possibly apply universal compression to $x^n$ and transmit to $C$ whereas the presence of memorized context at $R$ can possibly reduce the communication overhead in $S$-$R$ link. In this paper, we investigate the fundamental gain of the context memorization in the memory-assisted universal compression of the sequence $x^n$ over conventional universal source coding by providing a lower bound on the gain of memory-assisted source coding.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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-3 (induced by source paper "results on the fundamental gain of memory assisted universal source coding"):
  with members:
    - "memory assisted universal compression of network flows"
    - "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 "memory assisted universal compression of network flows"):
  with members:
    - "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)