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: "fulcrum network codes a code for fluid allocation of complexity"
  Abstract: This paper proposes Fulcrum network codes, a network coding framework that achieves three seemingly conflicting objectives: (i) to reduce the coding coefficient overhead to almost n bits per packet in a generation of n packets; (ii) to operate the network using only GF(2) operations at intermediate nodes if necessary, dramatically reducing complexity in the network; (iii) to deliver an end-to-end performance that is close to that of a high-field network coding system for high-end receivers while simultaneously catering to low-end receivers that decode in GF(2). As a consequence of (ii) and (iii), Fulcrum codes have a unique trait missing so far in the network coding literature: they provide the network with the flexibility to spread computational complexity over different devices depending on their current load, network conditions, or even energy targets in a decentralized way. At the core of our framework lies the idea of precoding at the sources using an expansion field GF(2h) to increase the number of dimensions seen by the network using a linear mapping. Fulcrum codes can use any high-field linear code for precoding, e.g., Reed-Solomon, with the structure of the precode determining some of the key features of the resulting code. For example, a systematic structure provides the ability to manage heterogeneous receivers while using the same data stream. Our analysis shows that the number of additional dimensions created during precoding controls the trade-off between delay, overhead, and complexity. Our implementation and measurements show that Fulcrum achieves similar decoding probability as high field Random Linear Network Coding (RLNC) approaches but with encoders/decoders that are an order of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "instantly decodable network coding for real time scalable video broadcast over wireless networks"):
  with members:
    - "resource allocation frameworks for network coded layered multimedia multicast services"
    - "adaptive prioritized random linear coding and scheduling for layered data delivery from multiple servers"
    - "coding based data broadcasting for time critical applications with rate adaptation"
    - "random linear network coding for wireless layered video broadcast general design methods for adaptive feedback free transmission"
    - "partially blind instantly decodable network codes for lossy feedback environment"
    - "network coding meets multimedia a review"
    - "joint coding and scheduling optimization in wireless systems with varying delay sensitivities"
    - "completion delay minimization for instantly decodable network codes"

Connected through hyperedge HE-2 (induced by source paper "a markov chain model for the decoding probability of sparse network coding"):
  with members:
    - "analysis and optimization of sparse random linear network coding for reliable multicast services"
    - "batched sparse codes"
    - "effects of mac approaches on non monotonic saturation with cope a simple case study"
    - "network coding meets tcp"

Connected through hyperedge HE-3 (induced by source paper "netcodccn a network coding approach for content centric networks"):
  with members:
    - "selection of network coding nodes for minimal playback delay in streaming overlays"

Connected through hyperedge HE-4 (induced by source paper "network coding over satcom lessons learned"):
  with members:
    - "low delay random linear coding over a stream"
    - "network coding as a wimax link reliability mechanism"
    - "speeding multicast by acknowledgment reduction technique smart"
    - "random linear network coding for time division duplexing when to stop talking and start listening"

Connected through hyperedge HE-5 (induced by source paper "adaptive prioritized random linear coding and scheduling for layered data delivery from multiple servers"):
  with members:
    - "on the fly erasure coding for real time video applications"

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)