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: "applications of derandomization theory in coding"
  Abstract: Randomized techniques play a fundamental role in theoretical computer science and discrete mathematics, in particular for the design of efficient algorithms and construction of combinatorial objects. The basic goal in derandomization theory is to eliminate or reduce the need for randomness in such randomized constructions. Towards this goal, numerous fundamental notions have been developed to provide a unified framework for approaching various derandomization problems and to improve our general understanding of the power of randomness in computation. Two important classes of such tools are pseudorandom generators and randomness extractors. Pseudorandom generators transform a short, purely random, sequence into a much longer sequence that looks random, while extractors transform a weak source of randomness into a perfectly random one (or one with much better qualities, in which case the transformation is called a randomness condenser). In this thesis, we explore some applications of the fundamental notions in derandomization theory to problems outside the core of theoretical computer science, and in particular, certain problems related to coding theory. First, we consider the wiretap channel problem which involves a communication system in which an intruder can eavesdrop a limited portion of the transmissions. We utilize randomness extractors to construct efficient and information-theoretically optimal communication protocols for this model. Then we consider the combinatorial group testing problem. In this classical problem, one aims to determine a set of defective items within a large population by asking a number of queries, where each query reveals whether a defective item is present within a specified group of items. We use randomness condensers to explicitly construct optimal, or nearly optimal, group testing schemes for a setting where the query outcomes can be highly unreliable, as well as the threshold model where a query returns positive if the number of defectives pass a certain threshold. Next, we use randomness condensers and extractors to design ensembles of error-correcting codes that achieve the information-theoretic capacity of a large class of communication channels, and then use the obtained ensembles for construction of explicit capacity achieving codes. Finally, we consider the problem of explicit construction of error-correcting codes on the Gilbert-Varshamov bound and extend the original idea of Nisan and Wigderson to obtain a small ensemble of codes, mostly achieving the bound, under suitable computational hardness assumptions.

Connected through hyperedge HE-1 (induced by source paper "nearly optimal deterministic algorithm for sparse walsh hadamard transform"):
  with members:
    - "polynomials a new tool for length reduction in binary discrete convolutions"
    - "nearly optimal sparse fourier transform"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-2 (induced by source paper "non malleable coding against bit wise and split state tampering"):
  with members:
    - "non malleable extractors and codes with their many tampered extensions"
    - "capacity of non malleable codes"
    - "non malleable extractors two source extractors and privacy amplification"
    - "privacy amplification and non malleable extractors via character sums"

Connected through hyperedge HE-3 (induced by source paper "restricted isometry of fourier matrices and list decodability of random linear codes"):
  with members:
    - "coding theoretic methods for sparse recovery"
    - "on the list decodability of random linear codes"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"
    - "list decoding tensor products and interleaved codes"

Connected through hyperedge HE-4 (induced by source paper "improved constructions for non adaptive threshold group testing"):
  with members:
    - "noise resilient group testing limitations and constructions"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-5 (induced by source paper "noise resilient group testing limitations and constructions"):
  with members:
    - "folded codes from function field towers and improved optimal rate list decoding"
    - "linear algebraic list decoding of folded reed solomon codes"
    - "subspace evasive sets"
    - "efficient and robust compressed sensing using high quality expander graphs"
    - "improved combinatorial group testing algorithms for real world problem sizes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

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.DM(Discrete Mathematics)