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: "eth hardness of approximating 2 csps and directed steiner network"
  Abstract: We study the 2-ary constraint satisfaction problems (2-CSPs), which can be stated as follows: given a constraint graph $G=(V,E)$, an alphabet set $\Sigma$ and, for each $\{u, v\}\in E$, a constraint $C_{uv} \subseteq \Sigma\times\Sigma$, the goal is to find an assignment $\sigma: V \to \Sigma$ that satisfies as many constraints as possible, where a constraint $C_{uv}$ is satisfied if $(\sigma(u),\sigma(v))\in C_{uv}$. #R##N#While the approximability of 2-CSPs is quite well understood when $|\Sigma|$ is constant, many problems are still open when $|\Sigma|$ becomes super constant. One such problem is whether it is hard to approximate 2-CSPs to within a polynomial factor of $|\Sigma| |V|$. Bellare et al. (1993) suggested that the answer to this question might be positive. Alas, despite efforts to resolve this conjecture, it remains open to this day. #R##N#In this work, we separate $|V|$ and $|\Sigma|$ and ask a related but weaker question: is it hard to approximate 2-CSPs to within a polynomial factor of $|V|$ (while $|\Sigma|$ may be super-polynomial in $|V|$)? Assuming the exponential time hypothesis (ETH), we answer this question positively by showing that no polynomial time algorithm can approximate 2-CSPs to within a factor of $|V|^{1 - o(1)}$. Note that our ratio is almost linear, which is almost optimal as a trivial algorithm gives a $|V|$-approximation for 2-CSPs. #R##N#Thanks to a known reduction, our result implies an ETH-hardness of approximating Directed Steiner Network with ratio $k^{1/4 - o(1)}$ where $k$ is the number of demand pairs. The ratio is roughly the square root of the best known ratio achieved by polynomial time algorithms (Chekuri et al., 2011; Feldman et al., 2012). #R##N#Additionally, under Gap-ETH, our reduction for 2-CSPs not only rules out polynomial time algorithms, but also FPT algorithms parameterized by $|V|$. Similar statement applies for DSN parameterized by $k$.

Connected through hyperedge HE-1 (induced by source paper "tight running time lower bounds for strong inapproximability of maximum k coverage unique set cover and related problems via t wise agreement testing theorem"):
  with members:
    - "a simple gap producing reduction for the parameterized set cover problem"
    - "parameterized intractability of even set and shortest vector problem"
    - "parameterized inapproximability of exact cover and nearest codeword"
    - "tight fpt approximations for k median and k means"
    - "agreement testing theorems on layered set systems"
    - "towards a general direct product testing theorem"
    - "gap s eth hardness of svp"
    - "from gap eth to fpt inapproximability clique dominating set and more"

Connected through hyperedge HE-2 (induced by source paper "treewidth pliability and ptas for max csps"):
  with members:
    - "thin graph classes and polynomial time approximation schemes"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "the complexity of general valued csps seen from the other side"
    - "a birthday repetition theorem and complexity of approximating dense csps"

Connected through hyperedge HE-3 (induced by source paper "a note on degree vs gap of min rep label cover and improved inapproximability for connectivity problems"):
  with members:
    - "approximation of non boolean 2csp"
    - "parameters of two prover one round game and the hardness of connectivity problems"
    - "approximation algorithms and hardness of the k route cut problem"
    - "an o k 3 log n approximation algorithm for vertex connectivity survivable network design"

Connected through hyperedge HE-4 (induced by source paper "parameterized approximation algorithms for directed steiner network problems"):
  with members:
    - "complexity of the steiner network problem with respect to the number of terminals"
    - "the complexity landscape of fixed parameter directed steiner network problems"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "on subexponential parameterized algorithms for steiner tree and directed subset tsp on planar graphs"
    - "parameterized complexity and approximability of directed odd cycle transversal"
    - "subexponential parameterized algorithms for planar and apex minor free graphs via low treewidth pattern covering"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "fixed parameter approximations for k center problems in low highway dimension graphs"

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.CC(Computational Complexity)