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: "when and by how much can helper node selection improve regenerating codes"
  Abstract: Regenerating codes (RCs) can significantly reduce the repair bandwidth of distributed storage networks. Initially, the analysis of RCs was based on the assumption that during the repair process, the newcomer does not distinguish (among all surviving nodes) which nodes to access, i.e., the newcomer is oblivious to the set of helpers being used. Such a scheme is termed the blind repair (BR) scheme. Nonetheless, it is intuitive in practice that the newcomer should access only those “good” helpers. This paper focuses on characterizing the effect of choosing the helper nodes in terms of the storage-bandwidth tradeoff. The results fully answer the following fundamental questions: Under what conditions does proactively choosing the helper nodes improve the storage-bandwidth tradeoff? Can this improvement be analytically quantified?

Connected through hyperedge HE-1 (induced by source paper "a connection between locally repairable codes and exact regenerating codes"):
  with members:
    - "when locally repairable codes meet regenerating codes what if some helpers are unavailable"
    - "layered exact repair regenerating codes via embedded error correction and block designs"
    - "locally repairable codes and matroid theory"
    - "codes between mbr and msr points with exact repair property"
    - "outer bounds for exact repair codes"
    - "optimal locally repairable and secure codes for distributed storage systems"
    - "new codes and inner bounds for exact repair in distributed storage systems"
    - "a family of optimal locally recoverable codes"

Connected through hyperedge HE-2 (induced by source paper "when locally repairable codes meet regenerating codes what if some helpers are unavailable"):
  with members:
    - "when can helper node selection improve regenerating codes part ii an explicit exact repair code construction"
    - "when can helper node selection improve regenerating codes part i graph based analysis"
    - "optimal locally repairable and secure codes for distributed storage systems"
    - "characterizing the rate region of the 4 3 3 exact repair regenerating codes"
    - "replication based storage systems with local repair"
    - "locally repairable codes with multiple repair alternatives"
    - "explicit mbr all symbol locality codes"
    - "distributed storage codes with repair by transfer and nonachievability of interior points on the storage bandwidth tradeoff"

Connected through hyperedge HE-3 (induced by source paper "on heterogeneous regenerating codes and capacity of distributed storage systems"):
  with members:
    - "new codes and inner bounds for exact repair in distributed storage systems"
    - "xoring elephants novel erasure codes for big data"
    - "capacity and security of heterogeneous distributed storage systems"
    - "explicit mbr all symbol locality codes"
    - "on weak dress codes for cloud storage"
    - "distributed storage codes with repair by transfer and nonachievability of interior points on the storage bandwidth tradeoff"
    - "codes with local regeneration"
    - "optimal exact regenerating codes for distributed storage at the msr and mbr points via a product matrix construction"

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)