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: "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"
  Abstract: To reduce computational complexity and delay in randomized network coded content distribution, and for some other practical reasons, coding is not performed simultaneously over all content blocks, but over much smaller, possibly overlapping subsets of these blocks, known as generations. A penalty of this strategy is throughput reduction. To analyze the throughput loss, we model coding over generations with random generation scheduling as a coupon collector's brotherhood problem. This model enables us to derive the expected number of coded packets needed for successful decoding of the entire content as well as the probability of decoding failure (the latter only when generations do not overlap) and further, to quantify the tradeoff between computational complexity and throughput. Interestingly, with a moderate increase in the generation size, throughput quickly approaches link capacity. Overlaps between generations can further improve throughput substantially for relatively small generation sizes.

Connected through hyperedge HE-1 (induced by source paper "on efficient decoding and design of sparse random linear network codes"):
  with members:
    - "network codes with overlapping chunks over line networks a case for linear time codes"
    - "sparse network coding with overlapping classes"

Connected through hyperedge HE-2 (induced by source paper "random linear fountain code with improved decoding success probability"):
  with members:
    - "on the concatenation of non binary random linear fountain codes with maximum distance separable codes"

Connected through hyperedge HE-3 (induced by source paper "perpetual codes for network coding"):
  with members:
    - "sparse network coding with overlapping classes"

Connected through hyperedge HE-4 (induced by source paper "batched sparse codes"):
  with members:
    - "overhead optimized gamma network codes"
    - "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-5 (induced by source paper "expander chunked codes"):
  with members:
    - "batched sparse codes"
    - "overhead optimized gamma network codes"
    - "linear complexity overhead optimized random linear network codes"
    - "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-6 (induced by source paper "linear complexity overhead optimized random linear network codes"):
  with members:
    - "overhead optimized gamma network codes"
    - "analysis of overlapped chunked codes with small chunks over line networks"
    - "sparse network coding with overlapping classes"

Connected through hyperedge HE-7 (induced by source paper "overhead optimized gamma network codes"):
  with members:
    - "sparse network coding with overlapping classes"

Connected through hyperedge HE-8 (induced by source paper "three schemes for wireless coded broadcast to heterogeneous users"):
  with members:
    - "rateless codes for single server streaming to diverse users"
    - "analysis of the second moment of the lt decoder"
    - "intermediate performance of rateless codes"

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)