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: "capacity achieving codes from randomness condensers"
  Abstract: We establish a general framework for construction of small ensembles of capacity achieving linear codes for a wide range of (not necessarily memoryless) discrete symmetric channels, and in particular, the binary erasure and symmetric channels. The main tool used in our constructions is the notion of randomness extractors and lossless condensers that are regarded as central tools in theoretical computer science. Same as random codes, the resulting ensembles preserve their capacity achieving properties under any change of basis. Using known explicit constructions of condensers, we obtain specific ensembles whose size is as small as polynomial in the block length. By applying our construction to Justesen's concatenation scheme (Justesen, 1972) we obtain explicit capacity achieving codes for BEC (resp., BSC) with almost linear time encoding and almost linear time (resp., quadratic time) decoding and exponentially small error probability.

Connected through hyperedge HE-1 (induced by source paper "correction of samplable additive errors"):
  with members:
    - "non malleable coding against bit wise and split state tampering"
    - "optimal rate code constructions for computationally simple channels"

Connected through hyperedge HE-2 (induced by source paper "sub linear time support recovery for compressed sensing using sparse graph codes"):
  with members:
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "computing a k sparse n length discrete fourier transform using at most 4k samples and o k log k complexity"
    - "sho fa robust compressive sensing with order optimal complexity measurements and bits"
    - "summary based structures with improved sublinear recovery for compressed sensing"
    - "phase retrieval via matrix completion"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimal phase transitions in compressed sensing"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "applications of derandomization theory in coding"):
  with members:
    - "graph constrained group testing"
    - "almost uniform sampling of points on high dimensional algebraic varieties"
    - "compressed sensing with probabilistic measurements a group testing solution"
    - "bit precision analysis for compressed sensing"
    - "noise resilient group testing limitations and constructions"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "invertible extractors and wiretap protocols"
    - "on wiretap networks ii"

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)