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: "overhead optimized gamma network codes"
  Abstract: We design a network coding scheme with minimum reception overhead and linear encoding/decoding complexity.

Connected through hyperedge HE-1 (induced by source paper "batched sparse codes"):
  with members:
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"
    - "on the capacity of non coherent network coding"
    - "coding for linear operator channels over finite fields"
    - "communication over finite field matrix channels"
    - "overlapped chunked network coding"
    - "sparse network coding with overlapping classes"
    - "compute and forward harnessing interference through structured codes"
    - "packing and covering properties of subspace codes for error control in random linear network coding"

Connected through hyperedge HE-2 (induced by source paper "structured random linear codes srlc bridging the gap between block and convolutional codes"):
  with members:
    - "finite length analysis of bats codes"
    - "on the fly erasure coding for real time video applications"

Connected through hyperedge HE-3 (induced by source paper "expander chunked codes"):
  with members:
    - "batched sparse codes"
    - "linear complexity overhead optimized random linear network codes"
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"
    - "throughput and latency in finite buffer line networks"
    - "overlapped chunked network coding"
    - "sparse network coding with overlapping classes"
    - "packing and covering properties of subspace codes for error control in random linear network coding"
    - "coding schemes for line networks"

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

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)