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: "sparse network coding with overlapping classes"
  Abstract: This paper presents a novel approach to network coding for distribution of large files. Instead of the usual approach of splitting packets into disjoint classes (also known as generations) we propose the use of overlapping classes. The overlapping allows the decoder to alternate between Gaussian elimination and back substitution, simultaneously boosting the performance and reducing the decoding complexity. Our approach can be seen as a combination of fountain coding and network coding. Simulation results are presented that demonstrate the promise of our approach.

Connected through hyperedge HE-1 (induced by source paper "upper bound scalability on achievable rates of batched codes for line networks"):
  with members:
    - "batched sparse codes"
    - "throughput and latency in finite buffer line networks"

Connected through hyperedge HE-2 (induced by source paper "delay optimal scheduling for chunked random linear network coding broadcast"):
  with members:
    - "optimal control for network coding broadcast"
    - "throughput of rateless codes over broadcast erasure channels"
    - "throughput delay analysis of random linear network coding for wireless broadcasting"

Connected through hyperedge HE-3 (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"
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"

Connected through hyperedge HE-4 (induced by source paper "the benefit of limited feedback to generation based random linear network coding in wireless broadcast"):
  with members:
    - "round robin streaming with generations"
    - "speeding multicast by acknowledgment reduction technique smart"
    - "overlapped chunked network coding"

Connected through hyperedge HE-5 (induced by source paper "perpetual codes for network coding"):
  with members:
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"

Connected through hyperedge HE-6 (induced by source paper "batched sparse codes"):
  with members:
    - "overhead optimized gamma network codes"
    - "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"
    - "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-7 (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"
    - "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"
    - "packing and covering properties of subspace codes for error control in random linear network coding"
    - "coding schemes for line networks"

Connected through hyperedge HE-8 (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"
    - "effects of the generation size and overlap on throughput and complexity in randomized linear network coding"

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)