human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "expander chunked codes"):
  Abstract: Chunked codes are efficient random linear network coding (RLNC) schemes with low computational cost, where the input packets are encoded into small chunks (i.e., subsets of the coded packets). During the network transmission, RLNC is performed within each chunk. In this paper, we first introduce a simple transfer matrix model to characterize the transmission of chunks, and derive some basic properties of the model to facilitate the performance analysis. We then focus on the design of overlapped chunked codes, a class of chunked codes whose chunks are non-disjoint subsets of input packets, which are of special interest since they can be encoded with negligible computational cost and in a causal fashion. We propose expander chunked (EC) codes, the first class of overlapped chunked codes that have an analyzable performance,where the construction of the chunks makes use of regular graphs. Numerical and simulation results show that in some practical settings, EC codes can achieve rates within 91 to 97 percent of the optimum and outperform the state-of-the-art overlapped chunked codes significantly.

Includes members:
  Member "batched sparse codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "upper bound scalability on achievable rates of batched codes for line networks")
  Member "overhead optimized gamma network codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "batched sparse codes")
  Member "linear complexity overhead optimized random linear network codes":
    also connected through other hyperedges:
      (none)
  Member "effects of the generation size and overlap on throughput and complexity in randomized linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on efficient decoding and design of sparse random linear network codes")
  Member "throughput and latency in finite buffer line networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "upper bound scalability on achievable rates of batched codes for line networks")
  Member "overlapped chunked network coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the benefit of limited feedback to generation based random linear network coding in wireless broadcast")
  Member "sparse network coding with overlapping classes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "upper bound scalability on achievable rates of batched codes for line networks")
  Member "packing and covering properties of subspace codes for error control in random linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "list and probabilistic unique decoding of folded subspace codes")

Question: Please tell me which class the source paper 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)