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: "using statistical encoding to achieve tree succinctness never seen before"
  Abstract: We propose a new succinct representation of labeled trees which represents a tree T using |T|H_k(T) number of bits (plus some smaller order terms), where |T|H_k(T) denotes the k-th order (tree label) entropy, as defined by Ferragina at al. 2005. Our representation employs a new, simple method of partitioning the tree, which preserves both tree shape and node degrees. Previously, the only representation that used |T|H_k(T) bits was based on XBWT, a transformation that linearizes tree labels into a single string, combined with compression boosting. The proposed representation is much simpler than the one based on XBWT, which used additional linear space (bounded by 0.01n) hidden in the "smaller order terms" notion, as an artifact of using zeroth order entropy coder; our representation uses sublinear additional space (for reasonable values of k and size of the label alphabet {\sigma}). The proposed representation can be naturally extended to a succinct data structure for trees, which uses |T|H_k(T) plus additional O(|T|k log_{\sigma}/ log_{\sigma} |T| + |T| log log_{\sigma} |T|/ log_{\sigma} |T|) bits and supports all the usual navigational queries in constant time. At the cost of increasing the query time to O(log log |T|/ log |T|) we can further reduce the space redundancy to O(|T| log log |T|/ log_{\sigma} |T|) bits, assuming k <= log_{\sigma} |T|. This is a major improvement over representation based on XBWT: even though XBWT-based representation uses |T|H_k(T) bits, the space needed for structure supporting navigational queries is much larger: (...)

Connected through hyperedge HE-1 (induced by source paper "entropy bounds for grammar based tree compressors"):
  with members:
    - "grammar based compression of unranked trees"
    - "entropy bounds for grammar compression"
    - "a universal tree balancing theorem"
    - "universal tree source coding using grammar based compression"
    - "a universal grammar based code for lossless compression of binary trees"
    - "large alphabets and incompressibility"
    - "identifying hierarchical structure in sequences a linear time algorithm"

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)