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: "network codes with overlapping chunks over line networks a case for linear time codes"
  Abstract: In this paper, the problem of designing network codes that are both communicationally and computationally efficient over packet line networks with worst-case schedules is considered. In this context, random linear network codes (dense codes) are asymptotically capacity-achieving, but require highly complex coding operations. To reduce the coding complexity, Maymounkov et al. proposed chunked codes (CC). Chunked codes operate by splitting the message into non-overlapping chunks and send a randomly chosen chunk at each transmission time by a dense code. The complexity, that is linear in the chunk size, is thus reduced compared to dense codes. In this paper, the existing analysis of CC is revised, and tighter bounds on the performance of CC are derived. As a result, we prove that (i) CC with sufficiently large chunks are asymptotically capacity-achieving, but with a slower speed of convergence compared to dense codes; and (ii) CC with relatively smaller chunks approach the capacity with an arbitrarily small but non-zero constant gap. To improve the speed of convergence of CC, while maintaining their advantage in reducing the computational complexity, we propose and analyze a new CC scheme with overlapping chunks, referred to as overlapped chunked codes (OCC). We prove that for smaller chunks, which are advantageous due to lower computational complexity, OCC with larger overlaps provide a better tradeoff between the speed of convergence and the message or packet error rate. This implies that for smaller chunks, and with the same computational complexity, OCC outperform CC in terms of the speed of approaching the capacity for sufficiently small target error rate. In fact, we design linear-time OCC with very small chunks (constant in the message size) that are both computationally and communicationally efficient, and that outperform linear-time CC.

Connected through hyperedge HE-1 (induced by source paper "on efficient decoding and design of sparse random linear network codes"):
  with members:
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"
    - "sparse network coding with overlapping classes"

Connected through hyperedge HE-2 (induced by source paper "coding delay analysis of chunked codes over line networks"):
  with members:
    - "how fast can dense codes achieve the min cut capacity of line networks"
    - "on the delay of network coding over line networks"
    - "sparse network coding with overlapping classes"
    - "coding schemes for line networks"

Connected through hyperedge HE-3 (induced by source paper "how fast can dense codes achieve the min cut capacity of line networks"):
  with members:
    - "on the delay of network coding over line networks"
    - "coding schemes for line networks"
    - "on coding for reliable communication over packet networks"

Connected through hyperedge HE-4 (induced by source paper "analysis of overlapped chunked codes with small chunks over line networks"):
  with members:
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"
    - "overlapped chunked network coding"
    - "sparse network coding with overlapping classes"
    - "coding schemes for line networks"

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)