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 "batched sparse codes"):
  Abstract: Network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing. However, using network coding usually incurs high computational and storage costs in the network devices and terminals. For example, some network coding schemes require the computational and/or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission, making such schemes difficult to be implemented in a router-like device that has only constant computational and storage capacities. In this paper, we introduce batched sparse code (BATS code), which enables a digital fountain approach to resolve the above issue. BATS code is a coding scheme that consists of an outer code and inner code. The outer code is a matrix generation of a fountain code. It works with the inner code that comprises random linear coding at the intermediate network nodes. BATS codes preserve such desirable properties of fountain codes as ratelessness and low encoding/decoding complexity. The computational and storage capacities of the intermediate network nodes required for applying BATS codes are independent of the number of packets for transmission. Almost capacity-achieving BATS code schemes are devised for unicast networks and certain multicast networks. For general multicast networks, under different optimization criteria, guaranteed decoding rates for the destination nodes can be obtained.

Includes members:
  Member "overhead optimized gamma network codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "structured random linear codes srlc bridging the gap between block and convolutional codes")
  Member "effects of the generation size and overlap on throughput and complexity in randomized linear network coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on efficient decoding and design of sparse random linear network codes")
  Member "on the capacity of non coherent network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on multiplicative matrix channels over finite chain rings")
  Member "coding for linear operator channels over finite fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi server coded caching")
  Member "communication over finite field matrix channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finite field matrix channels for network coding")
  Member "overlapped chunked network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "upper bound scalability on achievable rates of batched codes for line networks")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "private information retrieval over gaussian mac")

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)