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: "reducing a target interval to a few exact queries"
  Abstract: Many combinatorial problems involving weights can be formulated as a so-called ranged problem. That is, their input consists of a universe $U$, a (succinctly-represented) set family $\mathcal{F} \subseteq 2^{U}$, a weight function $\omega:U \rightarrow \{1,...,N\}$, and integers $0 \leq l \leq u \leq \infty$. Then the problem is to decide whether there is an $X \in \mathcal{F}$ such that $l \leq \sum_{e \in X}\omega(e) \leq u$. Well-known examples of such problems include Knapsack, Subset Sum, Maximum Matching, and Traveling Salesman. In this paper, we develop a generic method to transform a ranged problem into an exact problem (i.e. a ranged problem for which $l=u$). We show that our method has several intriguing applications in exact exponential algorithms and parameterized complexity, namely: #R##N#- In exact exponential algorithms, we present new insight into whether Subset Sum and Knapsack have efficient algorithms in both time and space. In particular, we show that the time and space complexity of Subset Sum and Knapsack are equivalent up to a small polynomial factor in the input size. We also give an algorithm that solves sparse instances of Knapsack efficiently in terms of space and time. - In parameterized complexity, we present the first kernelization results on weighted variants of several well-known problems. In particular, we show that weighted variants of Vertex Cover, Dominating Set, Traveling Salesman and Knapsack all admit polynomial randomized Turing kernels when parameterized by $|U|$. #R##N#Curiously, our method relies on a technique more commonly found in approximation algorithms.

Connected through hyperedge HE-1 (induced by source paper "equal subset sum faster than the meet in the middle"):
  with members:
    - "ppp completeness with connections to cryptography"
    - "a simple near linear pseudopolynomial time randomized algorithm for subset sum"
    - "subset sum made simple"
    - "seth based lower bounds for subset sum and bicriteria path"
    - "on problems as hard as cnf sat"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "a faster pseudopolynomial time algorithm for subset sum"

Connected through hyperedge HE-2 (induced by source paper "more consequences of falsifying seth and the orthogonal vectors conjecture"):
  with members:
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "conditional lower bounds for all pairs max flow"
    - "on the fine grained complexity of one dimensional dynamic programming"
    - "distributed pcp theorems for hardness of approximation in p"
    - "tree edit distance cannot be computed in strongly subcubic time unless apsp can"
    - "fine grained complexity of analyzing compressed data quantifying improvements over decompress and solve"
    - "improving viterbi is hard better runtimes imply faster clique algorithms"
    - "on the hardness of partially dynamic graph problems and connections to diameter"

Connected through hyperedge HE-3 (induced by source paper "seth based lower bounds for subset sum and bicriteria path"):
  with members:
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "a short note on merlin arthur protocols for subset sum"
    - "parameterizing pwm and profile matching and knapsack by the feasible weight solutions count"
    - "subquadratic algorithms for algebraic generalizations of 3sum"
    - "a near linear pseudopolynomial time algorithm for subset sum"
    - "a dichotomy for regular expression membership testing"
    - "improved bounds for 3sum k sum and linear degeneracy"
    - "clustered integer 3sum via additive combinatorics"

Connected through hyperedge HE-4 (induced by source paper "polynomial kernels for weighted problems"):
  with members:
    - "fixed parameter algorithms for minimum cost edge connectivity augmentation"
    - "on polynomial kernels for integer linear programs covering packing and feasibility"
    - "kernelization lower bounds by cross composition"
    - "parameterizing by the number of numbers"

Connected through hyperedge HE-5 (induced by source paper "losing weight by gaining edges"):
  with members:
    - "threesomes degenerates and love triangles"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "the parameterized complexity of domination type problems and application to linear codes"
    - "3sum 3xor triangles"
    - "exact weight subgraphs and the k sum conjecture"

Connected through hyperedge HE-6 (induced by source paper "on polynomial kernels for integer linear programs covering packing and feasibility"):
  with members:
    - "on polynomial kernels for sparse integer linear programs"
    - "hierarchies of inefficient kernelizability"
    - "vertex cover kernelization revisited upper and lower bounds for a refined parameter"
    - "cross composition a new technique for kernelization lower bounds"

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)