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: "index assignment for multichannel communication under failure"
  Abstract: We consider the problem of multiple description scalar quantizers and describing the achievable rate-distortion tuples in that setting. We formulate it as a combinatorial optimization problem of arranging numbers in a matrix to minimize the maximum difference between the largest and the smallest number in any row or column. We develop a technique for deriving lower bounds on the distortion at given channel rates. The approach is constructive, thus allowing an algorithm that gives a closely matching upper bound. For the case of two communication channels with equal rates, the bounds coincide, thus giving the precise lowest achievable distortion at fixed rates. The bounds are within a small constant for higher number of channels. To the best of our knowledge, this is the first result concerning systems with more than two communication channels. The problem is also equivalent to the bandwidth minimization problem of Hamming graphs.

Connected through hyperedge HE-1 (induced by source paper "optimal index assignment for multiple description scalar quantization"):
  with members:
    - "n channel asymmetric entropy constrained multiple description lattice vector quantization"
    - "n channel entropy constrained multiple description lattice vector quantization"
    - "vector gaussian multiple description with individual and central receivers"

Connected through hyperedge HE-2 (induced by source paper "n channel asymmetric entropy constrained multiple description lattice vector quantization"):
  with members:
    - "approximating the gaussian multiple description rate region under symmetric distortion constraints"
    - "lattices for distributed source coding jointly gaussian sources and reconstruction of a linear function"
    - "multiple description coding by dithered delta sigma quantization"
    - "multiple description lattice vector quantization"
    - "n channel entropy constrained multiple description lattice vector quantization"
    - "multiple description quantization via gram schmidt orthogonalization"
    - "vector gaussian multiple description with individual and central receivers"

Connected through hyperedge HE-3 (induced by source paper "multiple description quantization via gram schmidt orthogonalization"):
  with members:
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"
    - "robust distributed source coding"
    - "shannon meets wiener ii on mmse estimation in successive decoding schemes"
    - "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener"

Connected through hyperedge HE-4 (induced by source paper "n channel entropy constrained multiple description lattice vector quantization"):
  with members:
    - "n channel asymmetric multiple description lattice vector quantization"

Connected through hyperedge HE-5 (induced by source paper "optimal design of multiple description lattice vector quantizers"):
  with members:
    - "n channel entropy constrained multiple description lattice vector quantization"

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.DS(Data Structures and Algorithms)